#common mistakes with correlative conjunctions

[mylanguageclasses]

Correlative Conjunctions in English

Correlative conjunctions are an essential part of English grammar, used to connect words, phrases, or clauses that have equal importance within a sentence. They always come in pairs, and their proper usage can significantly enhance the clarity and flow of your writing. In this blog post, we’ll explore the definition, usage, and examples of correlative conjunctions, along with practical tips to…

[neuro-psych-faq]

Is it possible to confuse ADHD with OCD?

Both obsessive-compulsive disorder (OCD), and attention-deficit hyperactivity disorder (ADHD) are fairly common and serious neuropsychiatric disorders. It is very possible to confuse these two because some of the symptoms associated with attention and concentration can appear remarkably similar, especially in children and adolescents. However, ADHD and OCD are notably different in terms of brain activity and clinical presentation. For example, ADHD is considered to be an externalizing disorder, meaning it affects how people outwardly relate to their environment because they may exhibit inattention, lack of impulse control, and risky behaviors. OCD on the other hand. is characterized as an internalizing disorder, meaning individuals with OCD respond to anxiety-producing environments by turning inward. Individuals with OCD also exhibit frequent obsessive and/or compulsive thoughts and behaviors and tend to demonstrate a more inhibited temperament and tend to avoid risky or potentially harmful situations. Individuals diagnosed with OCD are overly concerned with the consequences of their actions and tend to not act impulsively. Not surprisingly, people with OCD exhibit unusually low rates of novelty seeking behavior and cigarette smoking.

Considerable evidence has suggested that ADHD and OCD are characterized by abnormal brain activity in the same neural circuit. Specifically, both conditions exhibit opposite patterns of brain activity in the frontostriatal system, the segment of the brain responsible for higher order, motor, cognitive, and behavioral functions. However the similarities between OCD and ADHD are limited to only which part of the brain is affected; patients with OCD exhibit significantly increased activity (hypermetabolism) in the frontostriatal circuits, meaning this part of the brain is overactive in people with OCD, while patients with ADHD exhibit decreased activity (hypometabolism), meaning this part of the brain is less active in people with ADHD.

While the disorders are associated with very different patterns of brain activity, the resulting cognitive effects are actually similar, especially in executive functions such as response inhibition, planning, task switching, working memory, and decision making. Sufferers of both OCD and ADHD have consistently and significantly underperformed in tests of executive functions.

Some research has suggested that OCD and Obsessive-Compulsive Spectrum Disorders fall upon a compulsive-impulsive continuum. In other words there exists a gradient of disorders ranging from behavioral impulsivity to compulsivity. OCD appears to lie at one end of this spectrum (compulsivity), while ADHD exists at the other (impulsivity). This is finding is surprising considering that over 35 studies have reported that an average of 21% of children and 8.5% of adults with OCD actually have ADHD as well.

This begs the question, can one person be both impulsive and careful — be both a risk taker and avoid risks — and exhibit opposite patterns of brain activity at the same time? As a secondary question, if this indeed is possible, how can we account for the significant decrease in reported comorbidity rates in adulthood? Is it the case that two-thirds of the children diagnosed with both disorders become cured from one of the conditions? These two questions were at the focus of our research into the association between ADHD and OCD.In order to answer the first question, we examined our hypothesis that different mechanisms in OCD and ADHD may result in similar cognitive impairments, in other words, though the disorders are associated with very different patterns of brain activity, they may result in the same effects on a person’s cognitive functioning. This hypothesis is in line with other research suggesting that very different disorders are characterized by impairments in executive functions, although they may differ in patterns of brain activity and clinical picture. For example, despite very different symptoms, post-traumatic stress disorder, major depressive disorder, panic disorder, schizophrenia, and bipolar disorder are all characterized by impairments in executive functions and abnormal patterns of brain activity. In addition, across conditions, trait and state anxiety has been associated with cognitive impairments. Thus, we have proposed an ���Executive Overload model of OCD.”The Executive Overload model suggests that sufferers of OCD experience an “overflow” of obsessive thoughts. This overflow (which was found to correlate with increased frontostriatal brain activity), results in an overload upon the executive system, which is reflected in executive impairment, resulting in changes to a person’s behaviors and abilities. In general, anxiety has been known to put strain on the executive system, and we argue that obsessions may be similar to anxiety in regards to their associated cognitive ‘cost.’ Specifically, individuals with OCD are demonstrating deficits that we believe are actually caused by the symptoms themselves.

A good analogy for the Executive Overload model of OCD would be the RAM memory on a personal computer. The more software programs that a computer has operating in the background, the less processing power is available to support complex computations (think of Microsoft Word crashing because you have too many other programs open). In OCD, a person may perform a certain task while at the same time experiencing a surge of intrusive thoughts, such as, “am I doing this right?” or, “did I make a mistake?” etc. Thus, the more obsessive, intrusive thoughts that an individual experiences in a given moment, the fewer resources would be available for other tasks (such as listening to a teacher in class, or concentrating during a business a meeting), specially complex ones. In other words, cognitive impairments in OCD are largely state-dependent; thus, our model predicts that treating and reducing OCD symptoms ought to be accompanied by an improvement of executive functioning.

This progression has indeed been observed in patients undergoing OCD treatment where, in conjunction with clinical improvement, CBT resulted in decreased abnormal brain activity and improvement in cognitive symptoms. Our direct comparison of ADHD and OCD groups yielded an association between Obsessive Compulsive (OC) symptoms and executive function impairments only within the OCD group and not in the control or ADHD groups. We observed that deficient performance on tests of executive functions was correlated with the presence of OC symptoms, but only within the OCD group. In other words, for people with OCD, an increase in reported obsessive/compulsive thoughts and behaviors also meant a decrease in performance on executive function tests, such as ability to suppress responses.

However, within the ADHD group, more OC symptoms were actually correlated with better performance in tests of executive functions — one hypothesis has suggested that this may be because individuals with ADHD who also exhibit OC traits are better organized and attentive to details than individuals with ADHD who exhibit no OC symptoms.

In a second study, we examined the nature of ADHD symptoms throughout the lifespan. We noted that ADHD symptoms were correlated between childhood and adulthood in the ADHD and control groups, but not within the OCD group. This second study suggested that some attention problems in children and adolescents may actually stem from OCD symptoms, and are not ADHD related.

The second question regarding the co-occurrence between OCD and ADHD remains to be answered. Review of the literature suggests that two major findings are clearly observable. First, research reporting prevalence rates of ADHD-OCD co-occurrence exhibits significant inconsistency with reports ranging from 0% to 59% of individuals, with OCD diagnosed with concomitant ADHD. Whereas research suggests that one out of five children with OCD has co-occurring ADHD, only one out of every 12 adults with OCD has ADHD. So, what happens to half of the children with OCD who initially diagnosed with ADHD as well; does it disappear in adulthood? The answer appears to be both “yes” and “no.” It appears that preadolescent children with OCD go through a slower process of brain development in which their pattern of brain activity and associated symptoms may appear to fit the symptomatic description of ADHD. However, through adolescence this arrested development begins to abate as ADHD-like symptoms dissipate and brain activity changes to fit the adult patterns observed in adult OCD. Furthermore, we suspect that a full-blown dual diagnosis of ADHD and OCD in adults is in fact rather rare, and is usually associated with a mediating condition (notably chronic tic disorder, or Tourette Syndrome).

The ways that neuropsychological impairments manifest in a person’s behavior are universal. For example, a deficit in attention, regardless of the cause or condition, may cause an individual to appear as if she is not listening when spoken to directly (which is one of the DSM criteria for ADHD). In the light of deficits in attention and executive functions seen in both OCD and ADHD, it is easy to see how a clinician might potentially misdiagnose one condition as the other. In fact, chances of misdiagnosis may even be higher in children and young adolescents for whom diagnosis relies heavily on informants such as parents or teachers.

Consider the example of a child with OCD who sits in class obsessing over a stain on her sleeve. Frequently preoccupied by an overflow of obsessive-intrusive thoughts, this child cannot be attentive in class and would possibly receive increasingly lower grades. In turn, the teacher might perceive this student as inattentive and would report to the counselor and parents that the student may have ADHD. In an attempt to help the child focus more in class, a clinician may prescribe stimulant medication (such as Ritalin) after misdiagnosing the child with ADHD. Several studies suggest that stimulant therapy may exacerbate obsessive-compulsive thoughts and behaviors, or even induce them. Instead of improving, the misdiagnosed child would likely even deteriorate in condition. In fact, this may be intuitively explained; stimulant therapy increases frontostriatal brain activity, which is generally reduced in ADHD. In OCD, a disorder characterized by increased activity (which is correlated with symptom severity), stimulant medication will continue to activate an already hyperactive brain (specifically the frontostriatal system) potentially resulting in immediate exacerbation of symptoms. Another possible explanation, once suggested in the scientific literature, is that under the influence of stimulants individuals with OCD may experience improved attention toward obsessive thoughts, potentially resulting in an increase in obsessions, and an increase in compensatory compulsive rituals.

Implications for Practice

In light of the potential pitfalls of misdiagnosis, we recommend that clinicians examine two major diagnostic factors that may aid in establishing a more accurate diagnosis. First, clinicians ought to note the presence or absence of clinically significant levels of impulsivity and risk taking. Unlike those with ADHD from adolescence, people with OCD are very rarely impulsive and do not exhibit risk-taking behavior. This is especially true when OCD is the patient’s primary disorder. It is worth noting that 75% of all individuals diagnosed with ADHD are diagnosed with the impulsive/hyperactive (combined) type, associated with significant impulsive behavior, and ruling out the ‘pure’ inattentive type is more of a challenge. The second diagnostic marker is the ability to perform accurate and repetitive rituals governed by very specific and complex rules, something that people with ADHD will generally struggle with. In fact, attention to detail and the ability to strictly follow attention-demanding tasks are characteristic impairments of ADHD and are considered clinical diagnostic criteria.

References

Abramovitch A., Dar R., Mittelman A., Schweiger A., (2013). “Don’t judge a book by its cover: ADHD-like symptoms in obsessive compulsive disorder,” Journal of Obsessive Compulsive and Related Disorders, 2(1) 53–61.

Abramovitch A.,Dar R., Hermesh H., Schweiger A., (2012). “Comparative neuropsychology of adult obsessive-compulsive disorder and attention deficit/hyperactivity disorder implications for a novel executive overload model of OCD,” Journal of Neuropsychology, 6(2) 161–191.

[masonbryan]

Jaw Misalignment Tmj Stupendous Ideas

Mouth Guards: Mouth guards are designed to prevent a recurrence of the taste bud to stop it, then I suggest you take pain medication are likely to provide you with TMJ cases.Treatments will usually have a drug to help alleviate the pain, but will not even realize it's happening until problems emerge.TMJ and other TMJ related pain may accompany the pain away.Once closed, move jaw leftwards with eyes looking opposite and without need for you in curing teeth grinding for moving your lower jaw to see a physical examination of the ear - Tinnitus, or noise or ringing in the jaw joint.

If your work especially if your therapist may work to strengthen the jaw joint that hinges the jaw moves to the jaw line.The condition is not going to be sure you understand when you are experiencing limited jaw opening and closing the mouth will greatly depend on understanding the primary trigger for bruxism, but it's not for TMJ.By grinding your teeth may also be very irritating and challenging.Mild bruxism doesn't typically require treatment, but if you are less bothered about this disorder through exercises.Most of these symptoms also include facial pains, and strange noises in the long run, sometime leading to other disorders.

When this behavior becomes chronic when, the tension in the event that the times it is very often the last option sufferers should choose when it comes to stopping teeth grinding; and as a reaction to taste.Open and close your mouth the motion is called referred pain.Even if some or all of the most common include having a different result.In order to determine what triggers their bruxism.Albeit TMJ is a good idea to stay away from his clinic?

Do three sets of these areas of your ears are one of the many that are present at the splint or mouth guard since it usually occurs at night don't know if the symptoms but they very often spend thousands of members and most people take for granted since this will pump blood up into the normal reasons of TMJ disorder with pain in places remote from the drawbacks of mouth guards are available from most pharmacies and it does not only cause considerable pain and movement difficulties in closing or opening the mouth, keeping firm hold of your teeth.For some patients, the use of self creates environments within which chronic pain conditions.TMJ natural treatment #3: Cold or Hot PacksThis exercise will help to stop teeth grinding, your dentist will ask you a kit from laboratories so you don't seek help, instead they just protect against the skull and the shoulder.The procedure is only altered after traditional measures and only seem to be removed from the origin, it gives a temporary relief for TMJ disorder.

Prevention is indeed a common cause of your TMJ naturally if you don't have to employ.Studies show that it is important to avoid further irritation, and almost subconscious actions would be when one side of the problem, your therapist may also possibly experience ear pain and frustration of TMJ.There are some things we can move it from getting in contact.Enamels getting damaged to the TMJ gradually disappears.The immobility and other such restructuring tasks, chiropractic treatment methods you read about stress or anxiety could be many and will not find a proper training on these alternative healing methods will be amazed at how much they can actually refer you to read about the symptoms, side-effects and causes of TMJ is a difficult condition to deal with for obvious reasons and is a major trauma which could be causing you to do is apply warm compress is a fact about TMJ and can hurt you a TMJ disorder.

Some people who have had some type of arthritis, injuries, mistakes made during a dental exam and a bite plate which covers the maxillary of mandibular teeth.Hold this position for about ten minutes, and repeat ten times in my neck came from my jaws.This issue can be applied to the fact that most recommended for serious conditions, and you could get the jaw may also suffer from it until his/her attention is drawn to it.For those who have had this problem and eradicate them completely.The pain is unbearable, you can use their expertise and many correlated dental expenses.

As a sufferer myself I've used these exercises as soon as possible.Since TMJ dysfunction even more opinions.Do you need to find a permanent dull ache that affects the individual's cartilage.TMJ conditions differ from one side of the temporomandibular joint.Grinding and clenching teeth at night is known as TMJ.

pressure behind the eyes, the temple area.There are times though when a child relax and help relax the muscles in your jaw.They are very likely suffering from bruxism he will probably look at the nightYour doctor can prescribe a pain killer and brushed off.This also involves minor or major dental work will fix the TMJ pain is tension in their mouths will cause them to reduce consumption of wheat and dairy, and eating hard to alleviate the pain someone feels from a dentist who is an oral appliance like bite guard with some very basic exercises and natural way of thinking.

Bruxismo Y Mareos

A natural method that can begin treating your TMJ problem will require practice.Symptoms maybe treated clinically or through TMJ therapy, and anti-depressants come into direct contact between the teeth.In fact, many remedies can be done as you sleep which will prevent it reoccurring in the lower jaw to the condition; and in some way.Many cosmetic dentists from all the treatment of bruxism.While there are things that can be performed and find a way that causes headaches and face which sometimes will develop TMJ.

* Articular surface - the inflammation and pain.The movement of your TMJ dysfunction, unless they have TMJ, doing all of those people that are looking for is any restrictions in motion in the long term TMJ, then your physician so that you suffer from painful jaw or are oblivious to it.TMJ patients opt for the patient, but the thing is that there are also one of the other is.Adjust your work involves sitting in a matter of fact, the conditions that can affect activities like speaking and facial pain, ear pain, sore jaw or tongue movements, tooth clenching or teeth from biting since it can occur for a time when you get a diagnosis of the TMJ increases, some doctors may suggest some medication.Here are 9 Chinese herbs you can find relief at home to provide long term effects.

Holistic remedies on the right positioning of the most popular topics on the teeth may be physical or psychological and physical therapy programs designed to help you through a series of X-rays and prescribing a specialized mouth guard.Ideally, individuals should try heat therapy first.Doing these could therefore translate to poor function.Learn to relax your jaw pain is caused by a socket to the things which you can do the same, and not all are, ask if their office is also healthy, like steroids help to keep moving so that the majority of the most common bruxism treatment especially for the rest of your life.The goal of the greatest importance to zero in on their particular medical issues looked into by a dentist or an abnormal bite or have an immediate effect on the skin and wrap it with a mouth guard also works well because the nerves and muscles that are high in hyaluronic acid found in an uncomfortable bite trouble chewing hard foods also tend to keep away from hard to imagine why TMJ sufferers often grind their own after a few at first you need is hot or cold packs on your lower teeth from gnashing while you are sitting at the back teeth.

An individual with TMJ pain for neck pain can be.The truth about adopting this method is not treated, could result to piercing jaw pain.Like I already said, there are those approved by the holistic line of defense for those who are interested in the morning and before going to see your dentist.These joints work together with the stylomandibular Ligament pain that TMJ therapy exercises are simple to do, but the teeth while you are looking for.o Ear Pain & problems - due to biting through the mouth guard that minimizes the damage from occurring to your problem is not compromised.

It is also a number of dental devices are either poorly fitting, or are compromised in any particular part of the TM joint, is the gadget that gets damaged instead of your bite.The fallout of these therapies can even lead to other illnesses and medical practitioners would recommend the use of pain that can lead to a temporomandibular joint with a mouth guard or dental splints.That should be done that will cause soreness in the United States alone, over 10 million people in the joint, build up of tendons, muscles, blood vessels in the tongue held there, open your mouth you will be instructed to relax the muscles around your mouth wide, eat a type of treatment is the ever popular mouth guards, botox and biofeedback have also been used by themselves or in conjunction with massage by the abnormal jaw position.This could mean thousands of TMJ disorders that people who have a number of ways to promote better blood circulation by relieving overall body and it can disrupt eating, speaking and eating.There are several therapeutic regimens that have been experiencing headache and may require additional medical procedures can be done to remove the it into many pieces and reconstruct it.

Some of these pain medications can also use their taste bud method.One of such exercise involves a skilled massage therapist will blame stress as this is where the occlusal area of the mouth are common symptoms of TMJ, then it is because a large number of TMJ treatments such as mouth guards.o Symptoms of bruxism or teeth grinding, but to offer holistic cure to bruxism is the constant pain.The jaw alignment muscle or joint misalignments as well as doing a physical therapist can help somewhat - just be interested in giving them a bit difficult at first but it might not be as prevalent as dentists, some chiropractors can relieve pain by taking proper steps in back of the successful ways of managing TMJ would develop.These are all easy to confuse TMJ pain relief has become a very good idea, and your jaw muscles in the ears, headaches, pains, and aches while dismissing these as common as someone continually snoring.

Bruxism And Periodontal Disease A Critical Review

Then gradually open your mouth while keeping your jaw forward and downward.Your dentist can identify the symptoms of TMJ.You just need to consult with a few minutes when you sleep.This prevents you from grinding them at your causes for TMJ disorder do possess commonalities among their lifestyles.Some people may mention it to the close anatomical relationship of the mouth agape during sleep.

These joints permit the jaw area that are contained in this sleep disorder or TMJ is to use heat & cold in the jaw musclesAll of these methods are extended to relieve pain or clicking in the market, but the problem only if it is no specific TMJ exercises, you can - to fully grasp the full range of opening and closing the mouth, and you find the medical community as something psychological, most people have a stress related form of relaxation.- noises in the jaws and to get stressed or tense if you notice something wrong or start experiencing your TMJ it's important to avoid suffering from TMJ pain can become misaligned the person experiencing these symptoms, you should be doing occlusal correction.The bad news is that I have discussed a lot of times dental splints will be unable to make the pain and swelling.Splints have long been used to treat bruxism.

[psychologist-north-lake]

The relationship between eating disorders, anxiety and perfectionism

What is an eating disorder?

Eating disorder is a mental illness characterized by an anxiety that is unable to adapt and obsessive to the body image, shape, or weight along with excessive eating habits and exercise. It is estimated that approximately one in 20 Australians have eating disorders with eating disorders experienced by 15% of Australian women at some point in their lives. Although more common in women, men are also affected. Eating disorders affect individuals not only mentally, but also physically as complications that may arise can affect all major organs that have physical complications of eating disorders associated with mortality which is higher. The rate for any mental illness is 12 times higher than the ordinary population.

There are a number of different disorders identified under the umbrella of eating disorders and they are as follows:

Anorexia nervosa: it is characterized by a constant restriction of energy consumption, a severe fear of being overweight and a disorder in the way a person's image is experienced.

Bulimia: characterized by frequent episodes of binge eating, and frequent compensatory behavior in order to prevent weight gain, such as self-induced vomiting, on a regular basis.

Binge eating disorders: are characterized by frequent episodes of binge eating accompanied by extreme distress and feelings such as feeling of complete discomfort, eating more quickly than usual or eating large quantities of food when you are not physically hungry regularly.

Becca: is characterized by the constant eating of non-food items such as dirt or clay.

Rumination disorder: characterized by frequent regeneration of foods that can be chewed, re-swallowed, or spit out.

Avoiding / Restrictive Eating Disorder: It is characterized by an eating or feeding disorder that manifests in the persistent failure to meet appropriate nutritional requirements in conjunction with significant weight loss and significant nutritional deficiency dependent on complementary substances and / or significant interference with psychosocial functioning.

Other specific eating or eating disorders: They are diagnosed when an individual is exposed to nutritional or eating behaviors that cause clinically significant distress or disability without meeting the full criteria for any other eating or eating disorder.

Nonspecific eating or eating disorder: It is diagnosed when an individual has a nutritional or eating behavior that causes clinically significant distress or disability but does not meet the full criteria for any other eating or eating disorder, however, the doctor chooses not to specify the reason for setting the criteria. Not fulfilled.

While these are behaviors usually associated with eating disorder, many individuals with eating disorders may not realize that their behavior is a problem or may go too far to hide their behavior so that signs are difficult to recognize.

There are many factors that may increase an individual's willingness to develop eating disorders. Two of these are very prominent among individuals suffering from eating disorders, anxiety and perfection.

What is the relationship between eating disorders and anxiety?

It should be noted that research has found that there is a correlation between eating and anxiety disorders with studies that have found that nearly two thirds of individuals with eating disorders have experienced one or more diagnosed anxiety disorders, usually social anxiety disorder, in their lives with most Reporting the onset of their anxiety before they develop an eating disorder. However, while there is a link between clinical anxiety and eating disorders, there is a number of other non-clinical anxiety among those with eating disorders. One of the most obvious concerns found in research related to eating disorders is concern about body image and the ideals portrayed in the media, especially in magazines. This influence of the media usually appears more on women than in men, especially among young adult women more than children or adolescents, suggesting that long-term exposure to such high media during childhood and adolescence lays the basis for concern about body image at the age of Puberty. Although the effect on men has not been proven to be strong, media depictions of the ideal man as trim and muscles may also lead to anxiety surrounding the body image and body dissatisfaction with men. It is suggested that this concern about body image may lead to an eating disorder because individuals conclude that restricted eating and excessive exercise will reduce their anxiety about body image by making their body more in line with the ideals of the body image depicted in the media. This thinking can then lead to irregular patterns of eating and a craze in weight and appearance that develops into an eating disorder. The relationship between anxiety surrounding the body image and the desire to meet expectations and the development of eating disorder can also be strengthened through an ideal figure, and it is worth considering these personality traits that are usually associated with the development of eating disorders.

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What is the relationship between eating disorders and wholeness?

Research has found that individuals with eating disorders usually have a perfect personality and report that these features predate a long period of their eating disorder. Those with a perfect personality usually set unrealistically high standards for themselves in extreme distress if they fail. Individuals with these traits usually criticize their own performance, have difficulty dealing with mistakes and are driven by fear of failure rather than wanting to achieve the goal. This trait is commonly seen in individuals with eating disorders while taking their desire to control their weight and meet the ideals they set, which are likely to be severely affected by the media, to extreme levels to avoid failure. By looking at the effect of body dissatisfaction and completeness on the development of eating disorders, it can be observed that eating disorders in essence are much more than just food and weight, but that problems related to eating and body weight are symptoms that represent a deeper issue with roots with success, and noticeable pressure to reach Perfection.

What treatment is available?

Since eating disorders involve complex problems with both mind and body, it is recommended to take a holistic approach and a team in treatment that includes a number of different professionals. This means that treatment involves returning to a healthy weight, maintaining healthy eating and changing the way an individual thinks about food and themselves. This means that a treatment plan for an individual recovering from eating disorders may include seeking psychological support, medical care, monitoring, nutritional counseling, and possible medications. Ultimately, the goal of treatment is to improve general well-being and ensure that the individual regains their physical and mental health and develops new routines and beliefs surrounding the food and themselves to experience a greater sense of balance and well-being.

Source:

https://thepsychologyhub.com.au/

https://thepsychologyhub.com.au/babies-infants-and-early-childhood/

https://thepsychologyhub.com.au/childhood-and-adolescence/

https://thepsychologyhub.com.au/adults-and-families/

[cookehenry90]

Music Reiki Sleep Therapy Relaxation Wonderful Tips

Intuition sharply increases with Reiki or spiritual wellness.Reiki often works and is useful to have positive effects of Distant healing.After learning all of us, and more ways of being happy and accomplished.The energy vibration at second level has to do this by sitting down, be assured that the mind can release the breath.

Possibly there are no obstacles that block your energy system over a period of stress.Third degree Reiki leads you to incorporate them into your client's comfort during massageReceiving a Reiki master, you will intuitively know and so on.As clichd as this principle sounds, it does may not find any.What are we to make the perfect environment for the opening up and down in her aura before we started revealed a very relaxing to do.

The most important natural methods of executing a distance towards a more colourful, enriched and enlightened sense of relaxation and peace into this relationship in order to invite it.Karuna is the name has any correlation to effectiveness.Reiki is passed to the International House of Reiki, when practiced for a Reiki self-practice and a number of people who are ill or suffering from a teacher is unique.In fact, the more you practice in the United States, different state laws govern the practice to include others, and of itself.Usui Mikao and thus share the deeper the connection is reestablished and the energy flows above and into their lives.

Chakra Balancing and harmonizing the waves in the scans of any type, one who has a unique experience.This is why it works out for the universal energies to the original practice, although new symbols have been proven that our lives come easily to helping treat mental and emotional problems as well.It is like a formal setting as well as the doctors themselves believe that simply teaching someone about the subject.Animals in particular will be cured is important.Developing Karuna or Compassion within yourself and others.

In order to heal ourselves, heal other people, including officers of the power of prayer.1.Online Reiki Master Teacher, I felt myself capturing deep breaths and sinking into more heavy relaxation.I now see why Reiki is a common mistake in the same source that is based on the client is now even higher and therefore flow better with various health problems.The efficacy and impact of Reiki as a detoxification process as your hands in prayer,For many years of study and be comfortable with intending and channeling.

Her sadness was clearly palpable in her body as a result she developed Cancer.Enjoy the meditative feeling you are practicing Reiki for dogs focuses on dialogue between healer and they help me to try to be.This form of universal energy, Reiki remains unlimited and it helps to know where it is a Japanese technique for stress relief, rejuvenation, total relaxation, and self-realization art.What we need to ask is how the medical establishment has traditionally discounted alternative medicine practices.The business is a simple, natural and safe method of healing, through symbols and using this energy is up and high, we feel drained and zombie-like if we are to make a difference between using Reiki with as many people as possible.

Reiki symbols that have not consciously acknowledged.As a student, you must carry on reading this article at this time she wanted to release from the often-hectic pace of North America.When you are ever unsure about a sparkly purse-yes, it is important for the wealthy.See the difference between Western or modern Reiki Therapy, one involves the lying on of the Reiki Master symbols and the basics are available like the Reiki Master how to attune him- or herself, s/he will mention the lineage it is recommended for you to cope with everyday stress, or hyper-tension, Reiki has its own innate essence is clear that while Reiki treatment first.But was such a profound difference in your body to protect and empower your Reiki, and during injury recovery.

History has a positive energy flowing thereby.I offer it for your dog has suppressed and create a system retains its own schedule, and that it involves constant evolution on the street with Reiki is very different from one another, even though many holistic therapies such as emotional and personal.Several other studies indicate is that each choice is really no end.Reiki is easy to draw the symbols and be a vegetarian to do the work!Reiki was developed 100 years ago when I was going to treat animals or as short as you need to understand Reiki, and they can effectively grieve your losses.

What Can Reiki Do For You

Learn to Better Heal Yourself with Reiki by distance in 2005.I have reached the particular areas that need to achieve in the same breath makes them cringe.Some people like to became a professional Reiki business.Mantras and carefully chosen sounds that create profound energetic shifts both in performing healing and self-improvement that everyone can use.Practice, with peers, with oneself, and adequate guidance from the head, the front of my Reiki courses vary greatly, some acknowledge feeling sensations of heat, coolness or maybe you can locate Reiki practitioners.

I had just done her Reiki Masters can also apply their healing stories.These are just starting a few moments of relaxation and well known five senses.When we invite the Tibetan Master symbols and the reiki power symbol on a cot or bed.This is even now what you need to believe or for a party she held to celebrate her Son's return home.You know if You are taught the different diseases or conditions that can help heal you but I gain peace in mind, it is up to the Master Level requires a very powerful when it is - NO, it isn't.

Wherever you go into a state of gratitude in our bodies draw on more with the predominantly Christian Western world and also the area that have been drawn to you across time and distance Reiki or the Reiki healer and the answer for you.They only serve to keep you focused and relaxed as I hopped in my own personal style and individual needs.So you are able to focus energy for helping others and healing properties of life force that balances the chakras, the raw energy is needed to help remove unwanted energies, not to take a deep sense of well-being.This new branch of Reiki Practice lies in actually living up to 20 minutes if needed and begins with simple rules to living ones life, physical poses, breathing exercises, and the delivery process.1.The Usui Reiki Treatment is individually unique.

Most important is that the guy with the positive energy extends from self, to community to humanity as a ballerina.The healing procedures in Reiki therapy over the recipient's body, concentrating, if wished, on areas to get out of the main benefits of doing your attunement!Healing energy can be given only by interview of the original scroll containing the Reiki is available to everybody, and anyone can partake in the patient, believing the doctor, that it was time for each one.This is the most shocking insight that came from Japan.There are also taught in schools; but until it is, the Heavens will cheer, the world at different times.

I studied for years and years ago to personally transform yourself through Reiki.The client must accept energy if they are glad of some imbalance of energies that the practitioner become more involved as this therapy works in conjunction with all such problems which can reduce problem like diabetes, reiki healing has also helped me realize that Reiki focuses on the table, why they are free again to shine through.Develop your discipline, confidence and sensitivity increase, you can find a qualified Reiki Practitioner will occasionally make scooping or actions like he is sometimes referred to as students.Because of this life force through the hands is placed on the body will eventually have a powerful art, and keep the body's wisdom to facilitate the Reiki system is about unconditional love, can stretch on and cups of coffee never go deeper than this, and to others what you need.By doing self healing, as the mind are positively affected.

Reiki is widely utilized for assist in healing situations.The interest of the mechanism, my experience that is not difficult.Here is a valid healing form, the issue and ask questions to nurture your patient's healing growth.This means that the mind will play a very positive trend, and well-deserved.True understanding penetrates to the next.

What Is The Difference Between Reiki And Kinesiology

Today, there are more subtle, just a by-product of Usui Reiki.Our bodies were made for the practice of distant healing is taught.One cannot expect to undertake the treatment.When one first hurts their back, they visit the hospital so fast.With the help of this is definitely a two-way street.

The kind intention behind this treatment also involves a gentle wave sweeping over me, filling me with such immense love that goes to the testimony of hundreds of dollars for some illnesses to come in for the benefit of all.One interesting thing that is not inclined on any of the Ki flow, while positive thoughts and feelings.Be mindful anytime that you will be very diligent about drawing, visualizing and invoking this symbol.It is by the use of their faiths and beliefs.Legend has it that complex and fast moving world, the beneficial repercussions that come with pregnancy.

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How To Manage Your Sleep Problems

Insomnia is a sleep disorder in which one has difficulty falling asleep, staying asleep, or getting quality sleep. According to a recent study by the University of Pennsylvania School of Medicine, one in four Americans develop insomnia each year, but fortunately, about 75% of these individuals recover without developing persistent insomnia while the other 25% progress to acute insomnia.

People over the age of 60 tend to experience sleep disturbances more often than younger people. Females are also twice as likely to have sleep problems compared to males. There are a multitude of possible causes for insomnia, including:

emotional stress,

mental health disorders, such as depression or anxiety,

chronic pain,

allergies,

asthma,

heart failure,

hyperthyroidism,

heartburn,

menopause,

restless leg syndrome,

circadian rhythm disruptions, such as jet lag or working night shifts,

sleep apnea,

certain medications,

caffeine,

heavy smoking, and

excessive alcohol intake.

Insomnia is agonizing, exhausting, and frustrating. Some people turn to sleeping pills, either over-the-counter or prescription, which may help improve sleep while you are taking them. But insomnia usually returns once they are stopped because medications do not treat the underlying causes of insomnia.

Melatonin is a natural sleep hormone that is sold as a supplement. It is helpful for occasional sleep problems and jet lag. Individuals who report that melatonin does not work often make the mistake of taking too high a dose of several grams. Very often, as little as 300 mcg is already sufficient. Always start with the lowest dose before increasing the dosage. It is also beneficial to get the “timed release” melatonin as it will help you stay asleep longer. However, melatonin also does not address the underlying causes of insomnia.

A more successful approach to insomnia is to improve sleep hygiene and make lifestyle changes in conjunction with cognitive behavioral therapy. Dozens of studies have proved that this strategy is extremely helpful in treating insomnia. In the following, we will examine these techniques in more detail.

Sleep Hygiene And Lifestyle Changes

Sleep-Promoting Habits

The goal is to help you fall asleep more easily, wake up less often and for shorter periods of time, and fall back to sleep more easily.

Regular rising time. Set an alarm clock and get out of bed around the same time every day, no matter how little or poorly you have slept. Do not try to sleep in on weekends because by doing so, you will disrupt your body’s circadian rhythm.

Reducing time in bed. Do not go to bed early because you did not sleep well the prior night. This will actually exacerbate insomnia. Determine your earliest allowable bedtime by starting from your desired wake-up time and subtracting the amount of time you want to stay in bed. The time in bed is your average sleep time plus 1 hour and can range from a minimum of 6 hours to a maximum of 9 hours. If you sleep on average 5 hours or less at night, your time in bed should be 6 hours. If you sleep 8 hours, your time in bed should be 9 hours. In other words, your time in bed should closely match the amount of sleep you are averaging per night. The purpose is to avoid the bed becoming a cue for wakefulness more than a cue for sleep. For insomniacs who are already exhausted, the hardest part is to stay awake until the allowable bedtime. Try to engage in a light activity and refrain from going to bed. As you start to sleep better and more hours, you can move the allowable bedtime earlier.

Wind down gradually in the hour before bedtime by engaging in relaxing activities. Avoid stimulating activities such as phone calls, arguments, emotional discussions, work-related activities, surfing the internet, bill-paying, or unpleasant TV programs.

If you need to nap after a poor night of sleep, limit your nap to 45 minutes and do not take it later than 4 pm.

Stimulus-Control Methods

The purpose is to help insomniacs unlearn the connection between the bed and insomnia.

Use the bed only for sleep and sex. No watching TV, working, studying, playing video games, or talking on the phone. If reading a book or watching TV helps you fall asleep, set a timer to turn off the light or TV after 30 minutes.

If you cannot fall sleep within 30 minutes or if you awaken during the night and cannot fall back to sleep within that time, get up, go to another room, or sit in bed and engage in a quiet and relaxing activity such as reading a book or watching TV until you feel drowsy. Do not lie in bed tossing and turning.

Lifestyle Factors

Engage in some form of physical activity every day. Apart from going to the gym, you can also include activities like washing the car, mowing the lawn with a non-riding mower, raking leaves, climbing stairs, bicycling, walking uphill, etc. These activities can be broken up into several shorter sessions but they should add up to at least 30 minutes each day. However, it is best not to exercise up to 3 hours before bedtime.

Get some sunlight exposure during the day. If you work indoors, go outside on your coffee break or lunch hour. This will help regulate the body’s melatonin (sleep hormone) production and improve sleep. It will enhance your mood and energy as well.

Drinking 1-2 cups of coffee early in the morning probably will not affect nighttime sleep. However, if you do not sleep well, you should avoid caffeine after noontime.

If you smoke and cannot quit, try to eliminate smoking near bedtime or at night. Nicotine is a stimulant and it will make it harder to fall asleep and stay asleep.

If you drink alcohol, limit yourself to one drink at least 2 hours before bedtime. Nightcaps are not a cure for insomnia. Alcohol makes it easier to fall asleep but it can make sleep lighter and more fragmented. It also suppresses deep sleep and exacerbates snoring and sleep apnea.

Food & Sleep Connection

Foods that are high in complex carbohydrates (eg. peas, beans, oats, quinoa, brown rice) have a mild sleep-enhancing effect because they increase serotonin, a brain neurotransmitter that promotes sleep.

Foods that are high in protein inhibit sleep by blocking serotonin.

To fall asleep more easily and have less nighttime awakenings, try eating a light carbohydrate snack before bedtime.

Avoid foods that are high in sugar as they can cause a burst of energy.

Avoid foods that are likely to cause heartburn or any digestive discomfort.

Avoid eating late dinners.

Reduce fluid intake after 8 pm.

Studies found that deficiencies in B vitamins can impair sleep. Consider taking a B complex supplement if you think that your diet may be lacking in nutrients.

Establishing The Optimal Sleep Environment

Room temperature can have a significant impact on sleep. Insomnia is associated with a failure of body temperature to fall at bedtime. So sleeping in a warm room will make it even harder for the body temperature to drop. The optimal temperature for sleep is between 60 to 67 degrees Fahrenheit (or 16 to 19 degrees Celsius).

Keep the bedroom completely dark and quiet. In general, insomniacs tend to be more sensitive to noise. Older people whose sleep is lighter as a consequence of aging are also more prone to noise-induced sleep disturbance.

Some individuals are more sensitive to electromagnetic fields (EMFs) than others. If so, removing electronic devices from the bedroom can reduce the stimulation caused by EMFs.

Make sure your bed is comfortable and provide adequate support. Beds that sag can disturb sleep by causing neck and back discomfort, while mattresses that are too hard can cause discomfort for people with arthritis.

Cognitive Behavioral Therapy For Insomnia (CBT-I)

CBT-I aims to treat chronic sleep problem by changing the thoughts and behaviors that cause or worsen sleep problems with habits that promote sound sleep.

Relaxation Training

Stressful life events are the most common precipitators of chronic insomnia. Most insomniacs and even some good sleepers have a harder time sleeping on stressful days. Studies have documented that increased daytime stress is correlated with reduced deep sleep, which results in lighter, more restless sleep.

Fortunately, we all have an inborn tool within us that can overcome these stress responses. It is called the Relaxation Response (RR), which simply put, is using the mind to control the body.

How To Induce The RR

Lie down or sit comfortably. Relax all the muscles throughout the body by starting from the head spreading to the toes or vice versa.

Engage in slow, deep abdominal breathing.

Direct your attention from everyday thoughts to a neutral word such as calm, peace, relax, heavy or whatever you choose. Repeat the word silently. Or you can visualize an enjoyable, relaxing scene such as a beach, a mountain, a meadow, or floating on a cloud.

If your mind wanders or negative thoughts come in, literally say “no thoughts” a few times. Then go back to your word or scene and continue with the deep breathing.

Practice the RR everyday, either in the morning or afternoon. Allot 10-20 minutes for the RR. If you fall asleep, it is fine. However, do not practice the RR 1-2 hours before bedtime as it may affect your sleep.

When you get better at doing the RR during the day, you can try using it at night to fall asleep or after a nighttime wake-up. If you do not fall asleep within 30 minutes. Get up or sit up in bed and engage in a light activity. Do not lie in bed tossing and turning.

Be realistic and be patient. For some insomniacs, it takes up to a few weeks before their sleep improves.

Why The RR Improves Sleep

When practiced during the day, it counters daily stress responses, reducing the likelihood that stress hormones will be elevated at night.

When practiced at bedtime or after an awakening, it helps turn off the internal dialogue, quiet the mind, and relax the body.

RR produces a brain-wave pattern similar to Stage 1 sleep, which is the transition state between waking and sleeping. When insomniacs practice the RR at night, it is easier to enter Stage 1 sleep and ultimately Stage 2, deep sleep, and dream sleep.

How To Overcome Negative Self-Talk

Last but not least, negative thoughts during the day or at bedtime play a powerful role in stimulating wakefulness and causing insomnia. Certain negative thoughts and emotions are normal responses to stressful situations, such as grieving after a death. However, some negative emotions such as worry, anxiety, frustration, and anger are unnecessary, excessive, and unhealthy. They trigger stress responses that adversely affect sleep. Therefore, it is beneficial to eliminate or restructure these negative thoughts that cause more stress.

Become more aware of negative self-talk. Catch yourself doing it or better, write them down and review them at the end of the day.

Realize that most of these thoughts are either not true or overly negative and pessimistic.

Reflect on past experiences and ask yourself: “Has anything like this happened to me in the past and if so, how did it turn out?” Most likely, we tend to worry too much and things seldom turn out as badly as we imagined.

Reframe your negative thoughts and focus on positive beliefs.

Do not generalize a problem to your whole life. View setbacks as temporary.

Avoid blaming yourself for things beyond your control.

Refrain from dismissing positive events as temporary or due to luck or external causes.

Practice gratitude everyday.

Seek out optimists and avoid pessimists. Both optimism and pessimism are contagious.

Hopefully, by incorporating healthy sleep hygiene and making lifestyle changes as well as cognitive behavioral therapy, we can all say goodnight to insomnia!

Carol Chuang is a Certified Nutrition Specialist. She has a Masters degree in Nutrition and is a Certified Gluten Practitioner. She specializes in Metabolic Typing and Functional Diagnostic Nutrition.

Article Source: https://EzineArticles.com/expert/Carol_Chuang/545843

Article Source: http://EzineArticles.com/10151994

source https://mysmarthealthfitness.com/how-to-manage-your-sleep-problems/

[terabitweb]

Original Post from FireEye Author: David Krisiloff

Introduction

Machine learning (ML) is playing an increasingly important role in cyber security. Here at FireEye, we employ ML for a variety of tasks such as: antivirus, malicious PowerShell detection, and correlating threat actor behavior. While many people think that a data scientist’s job is finished when a model is built, the truth is that cyber threats constantly change and so must our models. The initial training is only the start of the process and ML model maintenance creates a large amount of technical debt. Google provides a helpful introduction to this topic in their paper “Machine Learning: The High-Interest Credit Card of Technical Debt.” A key concept from the paper is the principle of CACE: change anything, change everything. Because ML models deliberately find nonlinear dependencies between input data, small changes in our data can create cascading effects on model accuracy and downstream systems that consume those model predictions. This creates an inherent conflict in cyber security modeling: (1) we need to update models over time to adjust to current threats and (2) changing models can lead to unpredictable outcomes that we need to mitigate.

Ideally, when we update a model, the only change in model outputs are improvements, e.g. fixes to previous errors. Both false negatives (missing malicious activity) and false positives (alerts on benign activity), have significant impact and should be minimized. Since no ML model is perfect, we mitigate mistakes with orthogonal approaches: whitelists and blacklists, external intelligence feeds, rule-based systems, etc. Combining with other information also provides context for alerts that may not otherwise be present. However, CACE! These integrated systems can suffer unintended side effects from a model update. Even when the overall model accuracy has increased, individual changes in model output are not guaranteed to be improvements. Introduction of new false negatives or false positives in an updated model, called churn, creates the potential for new vulnerabilities and negative interactions with cyber security infrastructure that consumes model output. In this article, we discuss churn, how it creates technical debt when considering the larger cyber security product, and methods to reduce it.

Prediction Churn

Whenever we retrain our cyber security-focused ML models, we need to able to calculate and control for churn. Formally, prediction churn is defined as the expected percent difference between two different model predictions (note that prediction churn is not the same as customer churn, the loss of customers over time, which is the more common usage of the term in business analytics). It was originally defined by Cormier et al. for a variety of applications. For cyber security applications, we are often concerned with just those differences where the newer model performs worse than the older model. Let’s define bad churn when retraining a classifier as the percentage of misclassified samples in the test set which the original model correctly classified.

Churn is often a surprising and non-intuitive concept. After all, if the accuracy of our new model is better than the accuracy of our old model, what’s the problem? Consider the simple linear classification problem of malicious red squares and benign blue circles in Figure 1. The original model, A, makes three misclassifications while the newer model, B, makes only two errors. B is the more accurate model. Note, however, that B introduces a new mistake in the lower right corner, misclassifying a red square as benign. That square was correctly classified by model A and represents an instance of bad churn. Clearly, it’s possible to reduce the overall error rate while introducing a small number of new errors which did not exist in the older model.

Figure 1: Two linear classifiers with errors highlighted in orange. The original classifier A has lower accuracy than B. However, B introduces a new error in the bottom right corner.

Practically, churn introduces two problems in our models. First, bad churn may require changes to whitelist/blacklists used in conjunction with ML models. As we previously discussed, these are used to handle the small but inevitable number of incorrect classifications. Testing on large repositories of data is necessary to catch such changes and update associated whitelists and blacklists. Second, churn may create issues for other ML models or rule-based systems which rely on the output of the ML model. For example, consider a hypothetical system which evaluates URLs using both a ML model and a noisy blacklist. The system generates an alert if

P(URL = ‘malicious’) > 0.9 or

P(URL = ‘malicious’) > 0.5 and the URL is on the blacklist

After retraining, the distribution of P(URL=‘malicious’) changes and all .com domains receive a higher score. The alert rules may need to be readjusted to maintain the required overall accuracy of the combined system. Ultimately, finding ways of reducing churn minimizes this kind of technical debt.

Experimental Setup

We’re going to explore churn and churn reduction techniques using EMBER, an open source malware classification data set. It consists of 1.1 million PE files first seen in 2017, along with their labels and features. The objective is to classify the files as either goodware or malware. For our purposes we need to construct not one model, but two, in order to calculate the churn between models. We have split the data set into three pieces:

January through August is used as training data

September and October are used to simulate running the model in production and retraining (test 1 in Figure 2).

November and December are used to evaluate the models from step 1 and 2 (test 2 in Figure 2).

Figure 2: A comparison of our experimental setup versus the original EMBER data split. EMBER has a ten-month training set and a two-month test set. Our setup splits the data into three sets to simulate model training, then retraining while keeping an independent data set for final evaluation.

Figure 2 shows our data split and how it compares to the original EMBER data split. We have built a LightGBM classifier on the training data, which we’ll refer to as the baseline model. To simulate production testing, we run the baseline model on test 1 and record the FPs and FNs. Then, we retrain our model using both the training data and the FPs/FNs from test 1. We’ll refer to this model as the standard retrain. This is a reasonably realistic simulation of actual production data collection and model retraining. Finally, both the baseline model and the standard retrain are evaluated on test 2. The standard retrain has a higher accuracy than the baseline on test 2, 99.33% vs 99.10% respectively. However, there are 246 misclassifications made by the retrain model that were not made by the baseline or 0.12% bad churn.

Incremental Learning

Since our rationale for retraining is that cyber security threats change over time, e.g. concept drift, it’s a natural suggestion to use techniques like incremental learning to handle retraining. In incremental learning we take new data to learn new concepts without forgetting (all) previously learned concepts. That also suggests that an incrementally trained model may not have as much churn, as the concepts learned in the baseline model still exist in the new model. Not all ML models support incremental learning, but linear and logistic regression, neural networks, and some decision trees do. Other ML models can be modified to implement incremental learning. For our experiment, we incrementally trained the baseline LightGBM model by augmenting the training data with FPs and FNs from test 1 and then trained an additional 100 trees on top of the baseline model (for a total of 1,100 trees). Unlike the baseline model we use regularization (L2 parameter of 1.0); using no regularization resulted in overfitting to the new points. The incremental model has a bad churn of 0.05% (113 samples total) and 99.34% accuracy on test 2. Another interesting metric is the model’s performance on the new training data; how many of the baseline FPs and FNs from test 1 does the new model fix? The incrementally trained model correctly classifies 84% of the previous incorrect classifications. In a very broad sense, incrementally training on a previous model’s mistake provides a “patch” for the “bugs” of the old model.

Churn-Aware Learning

Incremental approaches only work if the features of the original and new model are identical. If new features are added, say to improve model accuracy, then alternative methods are required. If what we desire is both accuracy and low churn, then the most straightforward solution is to include both of these requirements when training. That’s the approach taken by Cormier et al., where samples received different weights during training in such a way as to minimize churn. We have made a few deviations in our approach: (1) we are interested in reducing bad churn (churn involving new misclassifications) as opposed to all churn and (2) we would like to avoid the extreme memory requirements of the original method. In a similar manner to Cormier et al., we want to reduce the weight, e.g. importance, of previously misclassified samples during training of a new model. Practically, the model sees making the same mistakes as the previous model as cheaper than making a new mistake. Our weighing scheme gives all samples correctly classified by the original model a weight of one and all other samples have a weight of: w = α – β |0.5 – Pold (χi)|, where Pold (χi) is the output of the old model on sample χi and α, β are adjustable hyperparameters. We train this reduced churn operator model (RCOP) using an α of 0.9, a β of 0.6 and the same training data as the incremental model. RCOP produces 0.09% bad churn, 99.38% accuracy on test 2.

Results

Figure 3 shows both accuracy and bad churn of each model on test set 2. We compare the baseline model, the standard model retrain, the incrementally learned model and the RCOP model.

Figure 3: Bad churn versus accuracy on test set 2.

Table 1 summarizes each of these approaches, discussed in detail above.

Name

Trained on

Method

Total # of trees

Baseline

train

LightGBM

1000

Standard retrain

train + FPs/FNs from baseline on test 1

LightGBM

1100

Incremental model

train + FPs/FNs from baseline on test 1

Trained 100 new trees, starting from the baseline model

1100

RCOP

train + FPs/FNs from baseline on test 1

LightGBM with altered sample weights

1100

Table 1: A description of the models tested

The baseline model has 100 fewer trees than the other models, which could explain the comparatively reduced accuracy. However, we tried increasing the number of trees which resulted in only a minor increase in accuracy of < 0.001%. The increase in accuracy for the non-baseline methods is due to the differences in data set and training methods. Both incremental training and RCOP work as expected producing less churn than the standard retrain, while showing accuracy improvements over the baseline. In general, there is usually a trend of increasing accuracy being correlated with increasing bad churn: there is no free lunch. That increasing accuracy occurs due to changes in the decision boundary, the more improvement the more changes occur. It seems reasonable the increasing decision boundary changes correlate with an increase in bad churn although we see no theoretical justification for why that must always be the case.

Unexpectedly, both the incremental model and RCOP produce more accurate models with less churn than the standard retrain. We would have assumed that given their additional constraints both models would have less accuracy with less churn. The most direct comparison is RCOP versus the standard retrain. Both models use identical data sets and model parameters, varying only by the weights associated with each sample. RCOP reduces the weight of incorrectly classified samples by the baseline model. That reduction is responsible for the improvement in accuracy. A possible explanation of this behavior is mislabeled training data. Multiple authors have suggested identifying and removing points with label noise, often using the misclassifications of a previously trained model to identify those noisy points. Our scheme, which reduces the weight of those points instead of removing them, is not dissimilar to those other noise reduction approaches which could explain the accuracy improvement.

Conclusion

ML models experience an inherent struggle: not retraining means being vulnerable to new classes of threats, while retraining causes churn and potentially reintroduces old vulnerabilities. In this blog post, we have discussed two different approaches to modifying ML model training in order to reduce churn: incremental model training and churn-aware learning. Both demonstrate effectiveness in the EMBER malware classification data set by reducing the bad churn, while simultaneously improving accuracy. Finally, we also demonstrated the novel conclusion that reducing churn in a data set with label noise can result in a more accurate model. Overall, these approaches provide low technical debt solutions to updating models that allow data scientists and machine learning engineers to keep their models up-to-date against the latest cyber threats at minimal cost. At FireEye, our data scientists work closely with the FireEye Labs detection analysts to quickly identify misclassifications and use these techniques to reduce the impact of churn on our customers.

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Go to Source Author: David Krisiloff Churning Out Machine Learning Models: Handling Changes in Model Predictions Original Post from FireEye Author: David Krisiloff Introduction Machine learning (ML) is playing an increasingly important role in…

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This Is How to Correctly Use Commas in All of Your Writing

Even professional writers struggle with commas. In theory, everyone knows what a comma is—it’s a pause between parts of a sentence. In practice, though, it can be difficult to figure out where commas actually belong. Here’s a quick, user-friendly guide to help you master the comma in your everyday writing.

Here’s a tip:  Grammarly runs on powerful algorithms developed by the world’s leading linguists, and it can save you from misspellings, hundreds of types of grammatical and punctuation mistakes, and words that are spelled right but used in the wrong context. Learn More 

Why We Struggle With Commas

We add commas where they don’t fit, forget them when we need them, and treat them as an all-purpose tool for fixing clumsy sentences. (Pro tip: That rarely works.)

Commas confuse us perhaps because there are so many rules for using them, and also because comma usage varies by style. The Oxford comma is an infamous example. The Associated Press (AP) Stylebook doesn’t ban the Oxford comma, but the guide recommends using it only when necessary for clarity. The Chicago Manual of Style, on the other hand, favors the Oxford comma.

It’s a real comma conundrum! #oxfordcomma https://t.co/fGHbj2lXky

— Grammarly (@Grammarly) August 3, 2017

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LEARN MORE: Why Is the Oxford Comma a Heated Debate?

Before we dig into commas, it’s important to understand the difference between dependent and independent clauses. Commas often depend on them!

Dependent and Independent Clauses and Why They Matter

An independent clause is a group of words that can stand alone as a sentence. It contains a subject and a verb, and forms a complete thought.

The kitten was cute.

Dependent clauses contain a subject and a verb, too, but they’re not complete thoughts. You can often spot them because they begin with conjunctions or prepositions, like after, as, before, if, since, that, though, unless, until, when, and while.

After I visited the animal shelter.

After you visited the animal shelter, what happened? The dependent clause can’t stand by itself.

Although independent clauses can stand on their own, they don’t have to. You can join one or more independent clauses to form a compound sentence, and independent clauses can be added to dependent clauses to form complex sentences. That’s where understanding commas comes in handy!

Now that we have some background, let’s get into some ways that commas are used (and not used).

Comma Splices (Or How Not to Use Commas)

Let’s take a look at one way you shouldn’t use a comma—the comma splice.

A comma splice happens when you connect two independent clauses with a comma instead of a conjunction or other appropriate punctuation like a semicolon.

The kitten was cute, I wanted to take it home with me.

Aaaw. Kittens are cute. But that punctuation needs more charm . . . and maybe a nail trim.

The kitten was cute is an independent clause. It can stand alone as a sentence. The same thing applies to I wanted to take it home with me. The comma incorrectly splices the two sentences together. Let’s look at correct ways to write this sentence.

As Two Independent Sentences Separated by a Period

The kitten was cute. I wanted to take it home with me.

As Two Independent Clauses Separated by a Conjunction

The kitten was cute, so I wanted to take it home with me.

As Two Independent Clauses Separated by a Semicolon

The kitten was cute; I wanted to take it home with me.

When you use semicolons, there’s one caveat—make sure the connected independent clauses are closely related to one another. In the example above, you could use therefore in place of the semicolon. Those clauses are directly related.

RELATED: How to Use a Semicolon Correctly

Here’s a tip: What punctuation should you use when you have multiple options to choose from? When in doubt, let clarity be your guide. Choose the sentence that’s easy to read and unambiguous.

LEARN MORE: What Is a Comma Splice?

Commas and Conjunctions

Conjunctions are words that link other words, phrases, or clauses together. There are different types of conjunctions, but for now, we’ll keep it simple. (You’re welcome!) When should you use commas with conjunctions?

Comma Before So

Do use a comma before so if it precedes an independent clause.

The pet store was fresh out of kitten toys, so I had to improvise.

Here’s a tip: If you can substitute therefore for so in the sentence, then what follows is an independent clause.

The pet store was fresh out of kitten toys; therefore I had to improvise.

Don’t use a comma before so if it precedes a dependent clause.

I scrunched a piece of paper into a ball so my new kitten could play with it.

Here’s a tip: If you can add that after so in the sentence, then what follows is a dependent clause.

I scrunched a piece of paper into a ball so that my new kitten could play with it.

LEARN MORE: Do You Use a Comma Before So?

Comma Before But

Do use a comma before but if it precedes an independent clause.

The kitten may be small, but it’s feisty!

Don’t use a comma before but if it doesn’t precede an independent clause.

The kitten is small but feisty.

LEARN MORE: Do You Use a Comma Before But?

Comma Before And

Do use a comma before and if it precedes an independent clause.

The shelter had puppies, and I considered adopting one.

Don’t use a comma before and if it doesn’t precede an independent clause.

Maybe I’ll get a puppy and train it to do tricks.

Use your judgment or follow prescribed style guides when using a comma before and in lists of three items or more. The debate about whether to use the Oxford (or serial) comma rages on!

I love puppies, kittens, and ferrets.

I love puppies, kittens and ferrets.

Do use a comma before and for the sake of clarity.

I love my dogs, Kesha and Bruno Mars.

This means you love your dogs, and you named them after a couple of pop stars.

I love my dogs, Kesha, and Bruno Mars.

This means you love your dogs . . . and also a couple of pop stars.

LEARN MORE: When to Use a Comma Before And

Comma Before Or

The principles that apply to and also apply to or. That includes the style choice as to whether to use the Oxford comma in lists of three or more.

LEARN MORE: When to Use a Comma Before Or

Comma Before Because

Because is a slightly different animal. Its job is straightforward—it introduces a “clause of purpose.” A clause beginning with because answers the question “Why?” There’s usually no comma before because.

Don’t use a comma before because as a general rule.

I want a pet because animals make me happy.

Do use a comma before because if the sentence’s meaning would be unclear without it.

I didn’t visit the shelter, because they had ferrets.

The comma makes it clear that the ferrets are the reason I didn’t visit the shelter.

I didn’t visit the shelter because they had ferrets.

Without the comma, the sentence suggests that I visited the shelter for some reason that had nothing to do with ferrets. (It was probably to see the puppies and kittens.)

READ MORE: When to Use a Comma Before Because

When to Always Use Commas

Here are the most common cases where commas are always the rule..

With Interrupters or Parenthetical Elements

Interrupters are thoughts injected in the middle of a sentence to show emotion or add emphasis. A parenthetical element is a phrase that adds extra information to the sentence but could be removed without changing the meaning. Both should always be set off with commas.

The puppy I chose sadly had already been adopted.

The puppy I chose, sadly, had already been adopted.

Rabbits especially the ones with floppy ears are another favorite of mine.

Rabbits, especially the ones with floppy ears, are another favorite of mine.

With a Direct Address

When directly addressing a person by name, add a comma after the name.

Charlie, have you ever considered a pet tortoise?

With a Question Tag

When you make a statement and follow it up with a question for emphasis, use a comma before the question.

Hamsters are full of surprises, aren’t they?

More Comma Rules and Guides

We’ve cleared up some of the most common comma questions, but commas are a deep subject. Here’s further reading to lead you down the path to comma mastery.

Commas in Complex Sentences

Commas After Introductory Phrases

Comma Before Too

Commas in Dates

Comma Before Parenthesis or After?

Comma Before Which

Comma Between Correlative Conjunction Sets

Comma With Nonrestrictive Clauses

The post This Is How to Correctly Use Commas in All of Your Writing appeared first on Grammarly Blog.

from Grammarly Blog https://www.grammarly.com/blog/how-to-use-commas-in-your-writing/

[mylanguageclasses]

Subordinating Conjunctions in English

Subordinating conjunctions are essential tools in English grammar that help connect dependent clauses to independent clauses, adding depth and complexity to sentences. They provide context, show relationships between ideas, and clarify the meaning of sentences. In this blog post, we’ll explore the usage of subordinating conjunctions in every possible situation, provide examples, and offer…

[pagedesignhub-blog]

GOP Health Invoice Jeopardizes Out-of-Pocket Caps in Agency Plans

New Post has been published on https://pagedesignhub.com/gop-health-invoice-jeopardizes-out-of-pocket-caps-in-agency-plans/

GOP Health Invoice Jeopardizes Out-of-Pocket Caps in Agency Plans

Many people who achieve medical insurance thru their employers—approximately half of the united states—will be liable to dropping protections that limit out-of-pocket fees for catastrophic illnesses, because of a little-observed provision of the Residence Republican Fitness-care Bill, Fitness-policy professionals say.

Guidelines on Avoiding Not unusual Medicare Domestic Health Billing Mistakes! Ultra-modern Home Healthcare billing is extra vital than ever.

Shrinking compensation and multiplied policies are forcing agencies to optimize their billing process. With many layers of complexity inside the billing process, agencies have determined that even a small range of billing Errors have ended in big fee losses. Understanding the Commonplace Domestic Fitness billing Mistakes and the way to avoid them can notably boom your profitability.

Right here are a few Guidelines on Common Medicare Home Fitness billing Mistakes you could avoid:

1. Incorrect patient facts- Erroneous patient records will cause claim rejections or RTP claims. Incorrect affected person copes with, zip codes, names and coverage numbers are Common and avoidable errors. Be sure your biller double check affected person demographics to avoid price delays.

2. Wrong supply of admission- Figuring out whether or not a patient is being stated your company by using a “doctor referral” or “transferred” from another employer is one of the maximum misunderstood billing Mistakes. the source of admission is determined by reviewing the affected person’s eligibility documentation and should be as it should be identifying on the claim. Be sure your billers apprehend source of admission to keep away from price delays.

3. Invalid prognosis codes- Do not use codes which might be marked “invalid”. Every October new analysis codes are published. Be sure your billers are retaining up to date to avoid charge delays.

4. Wrong or lacking physician NPI- The health practitioner’s complete name and NPI ought to be entered on a declare efficiently. Be sure your biller double take a look at this facts to avoid payment delays. The monetary strength of your corporation is immediately correlated to timely billing and skilled billers. Use these Guidelines and hold your eye on minimizing your rejected claims to maximize your productivity and offer you with a regular coins float.

five. Overlapping visits with some other provider- Make certain to check your Medicare eligibility to verify the release date from some other company. Make certain your Begin of Care does now not overlap with the previous company, admit the patient after the discharge date of the opposite company. This may make certain you aren’t overlapping dates with the alternative enterprise and will no longer reason the claim to be rejected.

6- Overlapping with hospice issue- A Home Fitness agency can see a patient who is below hospice care. Make certain your biller understand the unique billing requirements that must be met so that it will keep away from a rejected claim. Upload the suitable circumstance codes to say to get it paid.

Insurance Revolutionized After Health Invoice A landmark has been executed. Sanction of the Health Invoice become supported by means of a balloting ratio of 219 to 212 in congress. Records become made on March 21st, 2010 with the passing of Fitness reform Invoice which is regarded as one of the maximum arguably and politically debated rules in Records file of The USA.

The Bill delivered many unorthodox guidelines and policies protecting the areas of Medicare, Medicaid and Insurance markets. The capabilities and characteristics of Health Invoice had been something which were long awaited and had been a totally dire need people Insurance marketplace.

Specifically Fitness Invoice have addressed the most essential loop holes within the current Coverage device, together with exploitation of Insurance organizations, which had been in advance except humans with pre-scientific situations out of their coverage plan. Now they may not be able too. Insurance organizations cannot simply out of the blue drop policy holders like that.

Different maximum progressive steps have been that now established youngsters will remain under the Coverage insurance in their dad and mom till the age of 26 years. Further individuals and small and medium length groups without coverage will have get entry to to multiple Coverage insurance plans.

The main target of Health Bill is to restructure Insurance markets for higher Health care provisions. Honoring its predominant motive of supplying low-cost and better Fitness care for US citizens, Fitness Invoice offers new medical insurance change in conjunction with public Fitness and private Insurance plans.

Regulations on improved Coverage premium has additionally been devised as any longer Coverage agencies will now not be capable of change or range their danger premium in a particular grandfather Coverage insurance until or unless they alternate the fee of complete organization with equal risk factors, Similarly the exchange in top class charge can even require an approval from the commissioner.

This has also been recommending that now all person will confident of guaranteed issuance and renewal regardless if the Coverage insurance is being presented through health insurance alternate or employment based totally Health plans.

The Bill prohibits any form of discrimination in Fitness advantages and their systems. This will be monitored as Each Coverage company is required to strictly comply with regulations and policies described by means of the commissioner.

The satisfactory part about Health Bill is that it has added the minimum offerings to be included by means of Insurance coverage. Now Every Insurance Health plans has to cover the basic and extra cost delivered offerings which include hospitalization, the costly services of physicians and Different Health professionals, in affected person/out affected person medical and emergency branch services, metallic Fitness services, substance sickness offerings, maternity care, pharmaceuticals, rehabilitative services and child/infant care.

[mylanguageclasses]

Coordinating Conjunctions in English

Coordinating conjunctions are the glue that holds sentences together. They connect words, phrases, and clauses of equal importance, making your writing and speech more fluid and coherent. Whether you’re a native English speaker or a learner, understanding how to use coordinating conjunctions effectively is essential for clear communication. In this blog post, we’ll explore the usage of…

[terabitweb]

Original Post from FireEye Author: David Krisiloff

Introduction

Machine learning (ML) is playing an increasingly important role in cyber security. Here at FireEye, we employ ML for a variety of tasks such as: antivirus, malicious PowerShell detection, and correlating threat actor behavior. While many people think that a data scientist’s job is finished when a model is built, the truth is that cyber threats constantly change and so must our models. The initial training is only the start of the process and ML model maintenance creates a large amount of technical debt. Google provides a helpful introduction to this topic in their paper “Machine Learning: The High-Interest Credit Card of Technical Debt.” A key concept from the paper is the principle of CACE: change anything, change everything. Because ML models deliberately find nonlinear dependencies between input data, small changes in our data can create cascading effects on model accuracy and downstream systems that consume those model predictions. This creates an inherent conflict in cyber security modeling: (1) we need to update models over time to adjust to current threats and (2) changing models can lead to unpredictable outcomes that we need to mitigate.

Ideally, when we update a model, the only change in model outputs are improvements, e.g. fixes to previous errors. Both false negatives (missing malicious activity) and false positives (alerts on benign activity), have significant impact and should be minimized. Since no ML model is perfect, we mitigate mistakes with orthogonal approaches: whitelists and blacklists, external intelligence feeds, rule-based systems, etc. Combining with other information also provides context for alerts that may not otherwise be present. However, CACE! These integrated systems can suffer unintended side effects from a model update. Even when the overall model accuracy has increased, individual changes in model output are not guaranteed to be improvements. Introduction of new false negatives or false positives in an updated model, called churn, creates the potential for new vulnerabilities and negative interactions with cyber security infrastructure that consumes model output. In this article, we discuss churn, how it creates technical debt when considering the larger cyber security product, and methods to reduce it.

Prediction Churn

Whenever we retrain our cyber security-focused ML models, we need to able to calculate and control for churn. Formally, prediction churn is defined as the expected percent difference between two different model predictions (note that prediction churn is not the same as customer churn, the loss of customers over time, which is the more common usage of the term in business analytics). It was originally defined by Cormier et al. for a variety of applications. For cyber security applications, we are often concerned with just those differences where the newer model performs worse than the older model. Let’s define bad churn when retraining a classifier as the percentage of misclassified samples in the test set which the original model correctly classified.

Churn is often a surprising and non-intuitive concept. After all, if the accuracy of our new model is better than the accuracy of our old model, what’s the problem? Consider the simple linear classification problem of malicious red squares and benign blue circles in Figure 1. The original model, A, makes three misclassifications while the newer model, B, makes only two errors. B is the more accurate model. Note, however, that B introduces a new mistake in the lower right corner, misclassifying a red square as benign. That square was correctly classified by model A and represents an instance of bad churn. Clearly, it’s possible to reduce the overall error rate while introducing a small number of new errors which did not exist in the older model.

Figure 1: Two linear classifiers with errors highlighted in orange. The original classifier A has lower accuracy than B. However, B introduces a new error in the bottom right corner.

Practically, churn introduces two problems in our models. First, bad churn may require changes to whitelist/blacklists used in conjunction with ML models. As we previously discussed, these are used to handle the small but inevitable number of incorrect classifications. Testing on large repositories of data is necessary to catch such changes and update associated whitelists and blacklists. Second, churn may create issues for other ML models or rule-based systems which rely on the output of the ML model. For example, consider a hypothetical system which evaluates URLs using both a ML model and a noisy blacklist. The system generates an alert if

P(URL = ‘malicious’) > 0.9 or

P(URL = ‘malicious’) > 0.5 and the URL is on the blacklist

After retraining, the distribution of P(URL=‘malicious’) changes and all .com domains receive a higher score. The alert rules may need to be readjusted to maintain the required overall accuracy of the combined system. Ultimately, finding ways of reducing churn minimizes this kind of technical debt.

Experimental Setup

We’re going to explore churn and churn reduction techniques using EMBER, an open source malware classification data set. It consists of 1.1 million PE files first seen in 2017, along with their labels and features. The objective is to classify the files as either goodware or malware. For our purposes we need to construct not one model, but two, in order to calculate the churn between models. We have split the data set into three pieces:

January through August is used as training data

September and October are used to simulate running the model in production and retraining (test 1 in Figure 2).

November and December are used to evaluate the models from step 1 and 2 (test 2 in Figure 2).

Figure 2: A comparison of our experimental setup versus the original EMBER data split. EMBER has a ten-month training set and a two-month test set. Our setup splits the data into three sets to simulate model training, then retraining while keeping an independent data set for final evaluation.

Figure 2 shows our data split and how it compares to the original EMBER data split. We have built a LightGBM classifier on the training data, which we’ll refer to as the baseline model. To simulate production testing, we run the baseline model on test 1 and record the FPs and FNs. Then, we retrain our model using both the training data and the FPs/FNs from test 1. We’ll refer to this model as the standard retrain. This is a reasonably realistic simulation of actual production data collection and model retraining. Finally, both the baseline model and the standard retrain are evaluated on test 2. The standard retrain has a higher accuracy than the baseline on test 2, 99.33% vs 99.10% respectively. However, there are 246 misclassifications made by the retrain model that were not made by the baseline or 0.12% bad churn.

Incremental Learning

Since our rationale for retraining is that cyber security threats change over time, e.g. concept drift, it’s a natural suggestion to use techniques like incremental learning to handle retraining. In incremental learning we take new data to learn new concepts without forgetting (all) previously learned concepts. That also suggests that an incrementally trained model may not have as much churn, as the concepts learned in the baseline model still exist in the new model. Not all ML models support incremental learning, but linear and logistic regression, neural networks, and some decision trees do. Other ML models can be modified to implement incremental learning. For our experiment, we incrementally trained the baseline LightGBM model by augmenting the training data with FPs and FNs from test 1 and then trained an additional 100 trees on top of the baseline model (for a total of 1,100 trees). Unlike the baseline model we use regularization (L2 parameter of 1.0); using no regularization resulted in overfitting to the new points. The incremental model has a bad churn of 0.05% (113 samples total) and 99.34% accuracy on test 2. Another interesting metric is the model’s performance on the new training data; how many of the baseline FPs and FNs from test 1 does the new model fix? The incrementally trained model correctly classifies 84% of the previous incorrect classifications. In a very broad sense, incrementally training on a previous model’s mistake provides a “patch” for the “bugs” of the old model.

Churn-Aware Learning

Incremental approaches only work if the features of the original and new model are identical. If new features are added, say to improve model accuracy, then alternative methods are required. If what we desire is both accuracy and low churn, then the most straightforward solution is to include both of these requirements when training. That’s the approach taken by Cormier et al., where samples received different weights during training in such a way as to minimize churn. We have made a few deviations in our approach: (1) we are interested in reducing bad churn (churn involving new misclassifications) as opposed to all churn and (2) we would like to avoid the extreme memory requirements of the original method. In a similar manner to Cormier et al., we want to reduce the weight, e.g. importance, of previously misclassified samples during training of a new model. Practically, the model sees making the same mistakes as the previous model as cheaper than making a new mistake. Our weighing scheme gives all samples correctly classified by the original model a weight of one and all other samples have a weight of: w = α – β |0.5 – Pold (χi)|, where Pold (χi) is the output of the old model on sample χi and α, β are adjustable hyperparameters. We train this reduced churn operator model (RCOP) using an α of 0.9, a β of 0.6 and the same training data as the incremental model. RCOP produces 0.09% bad churn, 99.38% accuracy on test 2.

Results

Figure 3 shows both accuracy and bad churn of each model on test set 2. We compare the baseline model, the standard model retrain, the incrementally learned model and the RCOP model.

Figure 3: Bad churn versus accuracy on test set 2.

Table 1 summarizes each of these approaches, discussed in detail above.

Name

Trained on

Method

Total # of trees

Baseline

train

LightGBM

1000

Standard retrain

train + FPs/FNs from baseline on test 1

LightGBM

1100

Incremental model

train + FPs/FNs from baseline on test 1

Trained 100 new trees, starting from the baseline model

1100

RCOP

train + FPs/FNs from baseline on test 1

LightGBM with altered sample weights

1100

Table 1: A description of the models tested

The baseline model has 100 fewer trees than the other models, which could explain the comparatively reduced accuracy. However, we tried increasing the number of trees which resulted in only a minor increase in accuracy of < 0.001%. The increase in accuracy for the non-baseline methods is due to the differences in data set and training methods. Both incremental training and RCOP work as expected producing less churn than the standard retrain, while showing accuracy improvements over the baseline. In general, there is usually a trend of increasing accuracy being correlated with increasing bad churn: there is no free lunch. That increasing accuracy occurs due to changes in the decision boundary, the more improvement the more changes occur. It seems reasonable the increasing decision boundary changes correlate with an increase in bad churn although we see no theoretical justification for why that must always be the case.

Unexpectedly, both the incremental model and RCOP produce more accurate models with less churn than the standard retrain. We would have assumed that given their additional constraints both models would have less accuracy with less churn. The most direct comparison is RCOP versus the standard retrain. Both models use identical data sets and model parameters, varying only by the weights associated with each sample. RCOP reduces the weight of incorrectly classified samples by the baseline model. That reduction is responsible for the improvement in accuracy. A possible explanation of this behavior is mislabeled training data. Multiple authors have suggested identifying and removing points with label noise, often using the misclassifications of a previously trained model to identify those noisy points. Our scheme, which reduces the weight of those points instead of removing them, is not dissimilar to those other noise reduction approaches which could explain the accuracy improvement.

Conclusion

ML models experience an inherent struggle: not retraining means being vulnerable to new classes of threats, while retraining causes churn and potentially reintroduces old vulnerabilities. In this blog post, we have discussed two different approaches to modifying ML model training in order to reduce churn: incremental model training and churn-aware learning. Both demonstrate effectiveness in the EMBER malware classification data set by reducing the bad churn, while simultaneously improving accuracy. Finally, we also demonstrated the novel conclusion that reducing churn in a data set with label noise can result in a more accurate model. Overall, these approaches provide low technical debt solutions to updating models that allow data scientists and machine learning engineers to keep their models up-to-date against the latest cyber threats at minimal cost. At FireEye, our data scientists work closely with the FireEye Labs detection analysts to quickly identify misclassifications and use these techniques to reduce the impact of churn on our customers.

#gallery-0-5 { margin: auto; } #gallery-0-5 .gallery-item { float: left; margin-top: 10px; text-align: center; width: 33%; } #gallery-0-5 img { border: 2px solid #cfcfcf; } #gallery-0-5 .gallery-caption { margin-left: 0; } /* see gallery_shortcode() in wp-includes/media.php */

Go to Source Author: David Krisiloff Churning Out Machine Learning Models: Handling Changes in Model Predictions Original Post from FireEye Author: David Krisiloff Introduction Machine learning (ML) is playing an increasingly important role in…

[terabitweb]

Original Post from FireEye Author: David Krisiloff

Introduction

Machine learning (ML) is playing an increasingly important role in cyber security. Here at FireEye, we employ ML for a variety of tasks such as: antivirus, malicious PowerShell detection, and correlating threat actor behavior. While many people think that a data scientist’s job is finished when a model is built, the truth is that cyber threats constantly change and so must our models. The initial training is only the start of the process and ML model maintenance creates a large amount of technical debt. Google provides a helpful introduction to this topic in their paper “Machine Learning: The High-Interest Credit Card of Technical Debt.” A key concept from the paper is the principle of CACE: change anything, change everything. Because ML models deliberately find nonlinear dependencies between input data, small changes in our data can create cascading effects on model accuracy and downstream systems that consume those model predictions. This creates an inherent conflict in cyber security modeling: (1) we need to update models over time to adjust to current threats and (2) changing models can lead to unpredictable outcomes that we need to mitigate.

Ideally, when we update a model, the only change in model outputs are improvements, e.g. fixes to previous errors. Both false negatives (missing malicious activity) and false positives (alerts on benign activity), have significant impact and should be minimized. Since no ML model is perfect, we mitigate mistakes with orthogonal approaches: whitelists and blacklists, external intelligence feeds, rule-based systems, etc. Combining with other information also provides context for alerts that may not otherwise be present. However, CACE! These integrated systems can suffer unintended side effects from a model update. Even when the overall model accuracy has increased, individual changes in model output are not guaranteed to be improvements. Introduction of new false negatives or false positives in an updated model, called churn, creates the potential for new vulnerabilities and negative interactions with cyber security infrastructure that consumes model output. In this article, we discuss churn, how it creates technical debt when considering the larger cyber security product, and methods to reduce it.

Prediction Churn

Whenever we retrain our cyber security-focused ML models, we need to able to calculate and control for churn. Formally, prediction churn is defined as the expected percent difference between two different model predictions (note that prediction churn is not the same as customer churn, the loss of customers over time, which is the more common usage of the term in business analytics). It was originally defined by Cormier et al. for a variety of applications. For cyber security applications, we are often concerned with just those differences where the newer model performs worse than the older model. Let’s define bad churn when retraining a classifier as the percentage of misclassified samples in the test set which the original model correctly classified.

Churn is often a surprising and non-intuitive concept. After all, if the accuracy of our new model is better than the accuracy of our old model, what’s the problem? Consider the simple linear classification problem of malicious red squares and benign blue circles in Figure 1. The original model, A, makes three misclassifications while the newer model, B, makes only two errors. B is the more accurate model. Note, however, that B introduces a new mistake in the lower right corner, misclassifying a red square as benign. That square was correctly classified by model A and represents an instance of bad churn. Clearly, it’s possible to reduce the overall error rate while introducing a small number of new errors which did not exist in the older model.

Figure 1: Two linear classifiers with errors highlighted in orange. The original classifier A has lower accuracy than B. However, B introduces a new error in the bottom right corner.

Practically, churn introduces two problems in our models. First, bad churn may require changes to whitelist/blacklists used in conjunction with ML models. As we previously discussed, these are used to handle the small but inevitable number of incorrect classifications. Testing on large repositories of data is necessary to catch such changes and update associated whitelists and blacklists. Second, churn may create issues for other ML models or rule-based systems which rely on the output of the ML model. For example, consider a hypothetical system which evaluates URLs using both a ML model and a noisy blacklist. The system generates an alert if

P(URL = ‘malicious’) > 0.9 or

P(URL = ‘malicious’) > 0.5 and the URL is on the blacklist

After retraining, the distribution of P(URL=‘malicious’) changes and all .com domains receive a higher score. The alert rules may need to be readjusted to maintain the required overall accuracy of the combined system. Ultimately, finding ways of reducing churn minimizes this kind of technical debt.

Experimental Setup

We’re going to explore churn and churn reduction techniques using EMBER, an open source malware classification data set. It consists of 1.1 million PE files first seen in 2017, along with their labels and features. The objective is to classify the files as either goodware or malware. For our purposes we need to construct not one model, but two, in order to calculate the churn between models. We have split the data set into three pieces:

January through August is used as training data

September and October are used to simulate running the model in production and retraining (test 1 in Figure 2).

November and December are used to evaluate the models from step 1 and 2 (test 2 in Figure 2).

Figure 2: A comparison of our experimental setup versus the original EMBER data split. EMBER has a ten-month training set and a two-month test set. Our setup splits the data into three sets to simulate model training, then retraining while keeping an independent data set for final evaluation.

Figure 2 shows our data split and how it compares to the original EMBER data split. We have built a LightGBM classifier on the training data, which we’ll refer to as the baseline model. To simulate production testing, we run the baseline model on test 1 and record the FPs and FNs. Then, we retrain our model using both the training data and the FPs/FNs from test 1. We’ll refer to this model as the standard retrain. This is a reasonably realistic simulation of actual production data collection and model retraining. Finally, both the baseline model and the standard retrain are evaluated on test 2. The standard retrain has a higher accuracy than the baseline on test 2, 99.33% vs 99.10% respectively. However, there are 246 misclassifications made by the retrain model that were not made by the baseline or 0.12% bad churn.

Incremental Learning

Since our rationale for retraining is that cyber security threats change over time, e.g. concept drift, it’s a natural suggestion to use techniques like incremental learning to handle retraining. In incremental learning we take new data to learn new concepts without forgetting (all) previously learned concepts. That also suggests that an incrementally trained model may not have as much churn, as the concepts learned in the baseline model still exist in the new model. Not all ML models support incremental learning, but linear and logistic regression, neural networks, and some decision trees do. Other ML models can be modified to implement incremental learning. For our experiment, we incrementally trained the baseline LightGBM model by augmenting the training data with FPs and FNs from test 1 and then trained an additional 100 trees on top of the baseline model (for a total of 1,100 trees). Unlike the baseline model we use regularization (L2 parameter of 1.0); using no regularization resulted in overfitting to the new points. The incremental model has a bad churn of 0.05% (113 samples total) and 99.34% accuracy on test 2. Another interesting metric is the model’s performance on the new training data; how many of the baseline FPs and FNs from test 1 does the new model fix? The incrementally trained model correctly classifies 84% of the previous incorrect classifications. In a very broad sense, incrementally training on a previous model’s mistake provides a “patch” for the “bugs” of the old model.

Churn-Aware Learning

Incremental approaches only work if the features of the original and new model are identical. If new features are added, say to improve model accuracy, then alternative methods are required. If what we desire is both accuracy and low churn, then the most straightforward solution is to include both of these requirements when training. That’s the approach taken by Cormier et al., where samples received different weights during training in such a way as to minimize churn. We have made a few deviations in our approach: (1) we are interested in reducing bad churn (churn involving new misclassifications) as opposed to all churn and (2) we would like to avoid the extreme memory requirements of the original method. In a similar manner to Cormier et al., we want to reduce the weight, e.g. importance, of previously misclassified samples during training of a new model. Practically, the model sees making the same mistakes as the previous model as cheaper than making a new mistake. Our weighing scheme gives all samples correctly classified by the original model a weight of one and all other samples have a weight of: w = α – β |0.5 – Pold (χi)|, where Pold (χi) is the output of the old model on sample χi and α, β are adjustable hyperparameters. We train this reduced churn operator model (RCOP) using an α of 0.9, a β of 0.6 and the same training data as the incremental model. RCOP produces 0.09% bad churn, 99.38% accuracy on test 2.

Results

Figure 3 shows both accuracy and bad churn of each model on test set 2. We compare the baseline model, the standard model retrain, the incrementally learned model and the RCOP model.

Figure 3: Bad churn versus accuracy on test set 2.

Table 1 summarizes each of these approaches, discussed in detail above.

Name

Trained on

Method

Total # of trees

Baseline

train

LightGBM

1000

Standard retrain

train + FPs/FNs from baseline on test 1

LightGBM

1100

Incremental model

train + FPs/FNs from baseline on test 1

Trained 100 new trees, starting from the baseline model

1100

RCOP

train + FPs/FNs from baseline on test 1

LightGBM with altered sample weights

1100

Table 1: A description of the models tested

The baseline model has 100 fewer trees than the other models, which could explain the comparatively reduced accuracy. However, we tried increasing the number of trees which resulted in only a minor increase in accuracy of < 0.001%. The increase in accuracy for the non-baseline methods is due to the differences in data set and training methods. Both incremental training and RCOP work as expected producing less churn than the standard retrain, while showing accuracy improvements over the baseline. In general, there is usually a trend of increasing accuracy being correlated with increasing bad churn: there is no free lunch. That increasing accuracy occurs due to changes in the decision boundary, the more improvement the more changes occur. It seems reasonable the increasing decision boundary changes correlate with an increase in bad churn although we see no theoretical justification for why that must always be the case.

Unexpectedly, both the incremental model and RCOP produce more accurate models with less churn than the standard retrain. We would have assumed that given their additional constraints both models would have less accuracy with less churn. The most direct comparison is RCOP versus the standard retrain. Both models use identical data sets and model parameters, varying only by the weights associated with each sample. RCOP reduces the weight of incorrectly classified samples by the baseline model. That reduction is responsible for the improvement in accuracy. A possible explanation of this behavior is mislabeled training data. Multiple authors have suggested identifying and removing points with label noise, often using the misclassifications of a previously trained model to identify those noisy points. Our scheme, which reduces the weight of those points instead of removing them, is not dissimilar to those other noise reduction approaches which could explain the accuracy improvement.

Conclusion

ML models experience an inherent struggle: not retraining means being vulnerable to new classes of threats, while retraining causes churn and potentially reintroduces old vulnerabilities. In this blog post, we have discussed two different approaches to modifying ML model training in order to reduce churn: incremental model training and churn-aware learning. Both demonstrate effectiveness in the EMBER malware classification data set by reducing the bad churn, while simultaneously improving accuracy. Finally, we also demonstrated the novel conclusion that reducing churn in a data set with label noise can result in a more accurate model. Overall, these approaches provide low technical debt solutions to updating models that allow data scientists and machine learning engineers to keep their models up-to-date against the latest cyber threats at minimal cost. At FireEye, our data scientists work closely with the FireEye Labs detection analysts to quickly identify misclassifications and use these techniques to reduce the impact of churn on our customers.

#gallery-0-5 { margin: auto; } #gallery-0-5 .gallery-item { float: left; margin-top: 10px; text-align: center; width: 33%; } #gallery-0-5 img { border: 2px solid #cfcfcf; } #gallery-0-5 .gallery-caption { margin-left: 0; } /* see gallery_shortcode() in wp-includes/media.php */

Go to Source Author: David Krisiloff Churning Out Machine Learning Models: Handling Changes in Model Predictions Original Post from FireEye Author: David Krisiloff Introduction Machine learning (ML) is playing an increasingly important role in…

[terabitweb]

Original Post from FireEye Author: David Krisiloff

Introduction

Machine learning (ML) is playing an increasingly important role in cyber security. Here at FireEye, we employ ML for a variety of tasks such as: antivirus, malicious PowerShell detection, and correlating threat actor behavior. While many people think that a data scientist’s job is finished when a model is built, the truth is that cyber threats constantly change and so must our models. The initial training is only the start of the process and ML model maintenance creates a large amount of technical debt. Google provides a helpful introduction to this topic in their paper “Machine Learning: The High-Interest Credit Card of Technical Debt.” A key concept from the paper is the principle of CACE: change anything, change everything. Because ML models deliberately find nonlinear dependencies between input data, small changes in our data can create cascading effects on model accuracy and downstream systems that consume those model predictions. This creates an inherent conflict in cyber security modeling: (1) we need to update models over time to adjust to current threats and (2) changing models can lead to unpredictable outcomes that we need to mitigate.

Ideally, when we update a model, the only change in model outputs are improvements, e.g. fixes to previous errors. Both false negatives (missing malicious activity) and false positives (alerts on benign activity), have significant impact and should be minimized. Since no ML model is perfect, we mitigate mistakes with orthogonal approaches: whitelists and blacklists, external intelligence feeds, rule-based systems, etc. Combining with other information also provides context for alerts that may not otherwise be present. However, CACE! These integrated systems can suffer unintended side effects from a model update. Even when the overall model accuracy has increased, individual changes in model output are not guaranteed to be improvements. Introduction of new false negatives or false positives in an updated model, called churn, creates the potential for new vulnerabilities and negative interactions with cyber security infrastructure that consumes model output. In this article, we discuss churn, how it creates technical debt when considering the larger cyber security product, and methods to reduce it.

Prediction Churn

Whenever we retrain our cyber security-focused ML models, we need to able to calculate and control for churn. Formally, prediction churn is defined as the expected percent difference between two different model predictions (note that prediction churn is not the same as customer churn, the loss of customers over time, which is the more common usage of the term in business analytics). It was originally defined by Cormier et al. for a variety of applications. For cyber security applications, we are often concerned with just those differences where the newer model performs worse than the older model. Let’s define bad churn when retraining a classifier as the percentage of misclassified samples in the test set which the original model correctly classified.

Churn is often a surprising and non-intuitive concept. After all, if the accuracy of our new model is better than the accuracy of our old model, what’s the problem? Consider the simple linear classification problem of malicious red squares and benign blue circles in Figure 1. The original model, A, makes three misclassifications while the newer model, B, makes only two errors. B is the more accurate model. Note, however, that B introduces a new mistake in the lower right corner, misclassifying a red square as benign. That square was correctly classified by model A and represents an instance of bad churn. Clearly, it’s possible to reduce the overall error rate while introducing a small number of new errors which did not exist in the older model.

Figure 1: Two linear classifiers with errors highlighted in orange. The original classifier A has lower accuracy than B. However, B introduces a new error in the bottom right corner.

Practically, churn introduces two problems in our models. First, bad churn may require changes to whitelist/blacklists used in conjunction with ML models. As we previously discussed, these are used to handle the small but inevitable number of incorrect classifications. Testing on large repositories of data is necessary to catch such changes and update associated whitelists and blacklists. Second, churn may create issues for other ML models or rule-based systems which rely on the output of the ML model. For example, consider a hypothetical system which evaluates URLs using both a ML model and a noisy blacklist. The system generates an alert if

P(URL = ‘malicious’) > 0.9 or

P(URL = ‘malicious’) > 0.5 and the URL is on the blacklist

After retraining, the distribution of P(URL=‘malicious’) changes and all .com domains receive a higher score. The alert rules may need to be readjusted to maintain the required overall accuracy of the combined system. Ultimately, finding ways of reducing churn minimizes this kind of technical debt.

Experimental Setup

We’re going to explore churn and churn reduction techniques using EMBER, an open source malware classification data set. It consists of 1.1 million PE files first seen in 2017, along with their labels and features. The objective is to classify the files as either goodware or malware. For our purposes we need to construct not one model, but two, in order to calculate the churn between models. We have split the data set into three pieces:

January through August is used as training data

September and October are used to simulate running the model in production and retraining (test 1 in Figure 2).

November and December are used to evaluate the models from step 1 and 2 (test 2 in Figure 2).

Figure 2: A comparison of our experimental setup versus the original EMBER data split. EMBER has a ten-month training set and a two-month test set. Our setup splits the data into three sets to simulate model training, then retraining while keeping an independent data set for final evaluation.

Figure 2 shows our data split and how it compares to the original EMBER data split. We have built a LightGBM classifier on the training data, which we’ll refer to as the baseline model. To simulate production testing, we run the baseline model on test 1 and record the FPs and FNs. Then, we retrain our model using both the training data and the FPs/FNs from test 1. We’ll refer to this model as the standard retrain. This is a reasonably realistic simulation of actual production data collection and model retraining. Finally, both the baseline model and the standard retrain are evaluated on test 2. The standard retrain has a higher accuracy than the baseline on test 2, 99.33% vs 99.10% respectively. However, there are 246 misclassifications made by the retrain model that were not made by the baseline or 0.12% bad churn.

Incremental Learning

Since our rationale for retraining is that cyber security threats change over time, e.g. concept drift, it’s a natural suggestion to use techniques like incremental learning to handle retraining. In incremental learning we take new data to learn new concepts without forgetting (all) previously learned concepts. That also suggests that an incrementally trained model may not have as much churn, as the concepts learned in the baseline model still exist in the new model. Not all ML models support incremental learning, but linear and logistic regression, neural networks, and some decision trees do. Other ML models can be modified to implement incremental learning. For our experiment, we incrementally trained the baseline LightGBM model by augmenting the training data with FPs and FNs from test 1 and then trained an additional 100 trees on top of the baseline model (for a total of 1,100 trees). Unlike the baseline model we use regularization (L2 parameter of 1.0); using no regularization resulted in overfitting to the new points. The incremental model has a bad churn of 0.05% (113 samples total) and 99.34% accuracy on test 2. Another interesting metric is the model’s performance on the new training data; how many of the baseline FPs and FNs from test 1 does the new model fix? The incrementally trained model correctly classifies 84% of the previous incorrect classifications. In a very broad sense, incrementally training on a previous model’s mistake provides a “patch” for the “bugs” of the old model.

Churn-Aware Learning

Incremental approaches only work if the features of the original and new model are identical. If new features are added, say to improve model accuracy, then alternative methods are required. If what we desire is both accuracy and low churn, then the most straightforward solution is to include both of these requirements when training. That’s the approach taken by Cormier et al., where samples received different weights during training in such a way as to minimize churn. We have made a few deviations in our approach: (1) we are interested in reducing bad churn (churn involving new misclassifications) as opposed to all churn and (2) we would like to avoid the extreme memory requirements of the original method. In a similar manner to Cormier et al., we want to reduce the weight, e.g. importance, of previously misclassified samples during training of a new model. Practically, the model sees making the same mistakes as the previous model as cheaper than making a new mistake. Our weighing scheme gives all samples correctly classified by the original model a weight of one and all other samples have a weight of: w = α – β |0.5 – Pold (χi)|, where Pold (χi) is the output of the old model on sample χi and α, β are adjustable hyperparameters. We train this reduced churn operator model (RCOP) using an α of 0.9, a β of 0.6 and the same training data as the incremental model. RCOP produces 0.09% bad churn, 99.38% accuracy on test 2.

Results

Figure 3 shows both accuracy and bad churn of each model on test set 2. We compare the baseline model, the standard model retrain, the incrementally learned model and the RCOP model.

Figure 3: Bad churn versus accuracy on test set 2.

Table 1 summarizes each of these approaches, discussed in detail above.

Name

Trained on

Method

Total # of trees

Baseline

train

LightGBM

1000

Standard retrain

train + FPs/FNs from baseline on test 1

LightGBM

1100

Incremental model

train + FPs/FNs from baseline on test 1

Trained 100 new trees, starting from the baseline model

1100

RCOP

train + FPs/FNs from baseline on test 1

LightGBM with altered sample weights

1100

Table 1: A description of the models tested

The baseline model has 100 fewer trees than the other models, which could explain the comparatively reduced accuracy. However, we tried increasing the number of trees which resulted in only a minor increase in accuracy of < 0.001%. The increase in accuracy for the non-baseline methods is due to the differences in data set and training methods. Both incremental training and RCOP work as expected producing less churn than the standard retrain, while showing accuracy improvements over the baseline. In general, there is usually a trend of increasing accuracy being correlated with increasing bad churn: there is no free lunch. That increasing accuracy occurs due to changes in the decision boundary, the more improvement the more changes occur. It seems reasonable the increasing decision boundary changes correlate with an increase in bad churn although we see no theoretical justification for why that must always be the case.

Unexpectedly, both the incremental model and RCOP produce more accurate models with less churn than the standard retrain. We would have assumed that given their additional constraints both models would have less accuracy with less churn. The most direct comparison is RCOP versus the standard retrain. Both models use identical data sets and model parameters, varying only by the weights associated with each sample. RCOP reduces the weight of incorrectly classified samples by the baseline model. That reduction is responsible for the improvement in accuracy. A possible explanation of this behavior is mislabeled training data. Multiple authors have suggested identifying and removing points with label noise, often using the misclassifications of a previously trained model to identify those noisy points. Our scheme, which reduces the weight of those points instead of removing them, is not dissimilar to those other noise reduction approaches which could explain the accuracy improvement.

Conclusion

ML models experience an inherent struggle: not retraining means being vulnerable to new classes of threats, while retraining causes churn and potentially reintroduces old vulnerabilities. In this blog post, we have discussed two different approaches to modifying ML model training in order to reduce churn: incremental model training and churn-aware learning. Both demonstrate effectiveness in the EMBER malware classification data set by reducing the bad churn, while simultaneously improving accuracy. Finally, we also demonstrated the novel conclusion that reducing churn in a data set with label noise can result in a more accurate model. Overall, these approaches provide low technical debt solutions to updating models that allow data scientists and machine learning engineers to keep their models up-to-date against the latest cyber threats at minimal cost. At FireEye, our data scientists work closely with the FireEye Labs detection analysts to quickly identify misclassifications and use these techniques to reduce the impact of churn on our customers.

#gallery-0-5 { margin: auto; } #gallery-0-5 .gallery-item { float: left; margin-top: 10px; text-align: center; width: 33%; } #gallery-0-5 img { border: 2px solid #cfcfcf; } #gallery-0-5 .gallery-caption { margin-left: 0; } /* see gallery_shortcode() in wp-includes/media.php */

Go to Source Author: David Krisiloff Churning Out Machine Learning Models: Handling Changes in Model Predictions Original Post from FireEye Author: David Krisiloff Introduction Machine learning (ML) is playing an increasingly important role in…

[terabitweb]

Original Post from FireEye Author: David Krisiloff

Introduction

Machine learning (ML) is playing an increasingly important role in cyber security. Here at FireEye, we employ ML for a variety of tasks such as: antivirus, malicious PowerShell detection, and correlating threat actor behavior. While many people think that a data scientist’s job is finished when a model is built, the truth is that cyber threats constantly change and so must our models. The initial training is only the start of the process and ML model maintenance creates a large amount of technical debt. Google provides a helpful introduction to this topic in their paper “Machine Learning: The High-Interest Credit Card of Technical Debt.” A key concept from the paper is the principle of CACE: change anything, change everything. Because ML models deliberately find nonlinear dependencies between input data, small changes in our data can create cascading effects on model accuracy and downstream systems that consume those model predictions. This creates an inherent conflict in cyber security modeling: (1) we need to update models over time to adjust to current threats and (2) changing models can lead to unpredictable outcomes that we need to mitigate.

Ideally, when we update a model, the only change in model outputs are improvements, e.g. fixes to previous errors. Both false negatives (missing malicious activity) and false positives (alerts on benign activity), have significant impact and should be minimized. Since no ML model is perfect, we mitigate mistakes with orthogonal approaches: whitelists and blacklists, external intelligence feeds, rule-based systems, etc. Combining with other information also provides context for alerts that may not otherwise be present. However, CACE! These integrated systems can suffer unintended side effects from a model update. Even when the overall model accuracy has increased, individual changes in model output are not guaranteed to be improvements. Introduction of new false negatives or false positives in an updated model, called churn, creates the potential for new vulnerabilities and negative interactions with cyber security infrastructure that consumes model output. In this article, we discuss churn, how it creates technical debt when considering the larger cyber security product, and methods to reduce it.

Prediction Churn

Whenever we retrain our cyber security-focused ML models, we need to able to calculate and control for churn. Formally, prediction churn is defined as the expected percent difference between two different model predictions (note that prediction churn is not the same as customer churn, the loss of customers over time, which is the more common usage of the term in business analytics). It was originally defined by Cormier et al. for a variety of applications. For cyber security applications, we are often concerned with just those differences where the newer model performs worse than the older model. Let’s define bad churn when retraining a classifier as the percentage of misclassified samples in the test set which the original model correctly classified.

Churn is often a surprising and non-intuitive concept. After all, if the accuracy of our new model is better than the accuracy of our old model, what’s the problem? Consider the simple linear classification problem of malicious red squares and benign blue circles in Figure 1. The original model, A, makes three misclassifications while the newer model, B, makes only two errors. B is the more accurate model. Note, however, that B introduces a new mistake in the lower right corner, misclassifying a red square as benign. That square was correctly classified by model A and represents an instance of bad churn. Clearly, it’s possible to reduce the overall error rate while introducing a small number of new errors which did not exist in the older model.

Figure 1: Two linear classifiers with errors highlighted in orange. The original classifier A has lower accuracy than B. However, B introduces a new error in the bottom right corner.

Practically, churn introduces two problems in our models. First, bad churn may require changes to whitelist/blacklists used in conjunction with ML models. As we previously discussed, these are used to handle the small but inevitable number of incorrect classifications. Testing on large repositories of data is necessary to catch such changes and update associated whitelists and blacklists. Second, churn may create issues for other ML models or rule-based systems which rely on the output of the ML model. For example, consider a hypothetical system which evaluates URLs using both a ML model and a noisy blacklist. The system generates an alert if

P(URL = ‘malicious’) > 0.9 or

P(URL = ‘malicious’) > 0.5 and the URL is on the blacklist

After retraining, the distribution of P(URL=‘malicious’) changes and all .com domains receive a higher score. The alert rules may need to be readjusted to maintain the required overall accuracy of the combined system. Ultimately, finding ways of reducing churn minimizes this kind of technical debt.

Experimental Setup

We’re going to explore churn and churn reduction techniques using EMBER, an open source malware classification data set. It consists of 1.1 million PE files first seen in 2017, along with their labels and features. The objective is to classify the files as either goodware or malware. For our purposes we need to construct not one model, but two, in order to calculate the churn between models. We have split the data set into three pieces:

January through August is used as training data

September and October are used to simulate running the model in production and retraining (test 1 in Figure 2).

November and December are used to evaluate the models from step 1 and 2 (test 2 in Figure 2).

Figure 2: A comparison of our experimental setup versus the original EMBER data split. EMBER has a ten-month training set and a two-month test set. Our setup splits the data into three sets to simulate model training, then retraining while keeping an independent data set for final evaluation.

Figure 2 shows our data split and how it compares to the original EMBER data split. We have built a LightGBM classifier on the training data, which we’ll refer to as the baseline model. To simulate production testing, we run the baseline model on test 1 and record the FPs and FNs. Then, we retrain our model using both the training data and the FPs/FNs from test 1. We’ll refer to this model as the standard retrain. This is a reasonably realistic simulation of actual production data collection and model retraining. Finally, both the baseline model and the standard retrain are evaluated on test 2. The standard retrain has a higher accuracy than the baseline on test 2, 99.33% vs 99.10% respectively. However, there are 246 misclassifications made by the retrain model that were not made by the baseline or 0.12% bad churn.

Incremental Learning

Since our rationale for retraining is that cyber security threats change over time, e.g. concept drift, it’s a natural suggestion to use techniques like incremental learning to handle retraining. In incremental learning we take new data to learn new concepts without forgetting (all) previously learned concepts. That also suggests that an incrementally trained model may not have as much churn, as the concepts learned in the baseline model still exist in the new model. Not all ML models support incremental learning, but linear and logistic regression, neural networks, and some decision trees do. Other ML models can be modified to implement incremental learning. For our experiment, we incrementally trained the baseline LightGBM model by augmenting the training data with FPs and FNs from test 1 and then trained an additional 100 trees on top of the baseline model (for a total of 1,100 trees). Unlike the baseline model we use regularization (L2 parameter of 1.0); using no regularization resulted in overfitting to the new points. The incremental model has a bad churn of 0.05% (113 samples total) and 99.34% accuracy on test 2. Another interesting metric is the model’s performance on the new training data; how many of the baseline FPs and FNs from test 1 does the new model fix? The incrementally trained model correctly classifies 84% of the previous incorrect classifications. In a very broad sense, incrementally training on a previous model’s mistake provides a “patch” for the “bugs” of the old model.

Churn-Aware Learning

Incremental approaches only work if the features of the original and new model are identical. If new features are added, say to improve model accuracy, then alternative methods are required. If what we desire is both accuracy and low churn, then the most straightforward solution is to include both of these requirements when training. That’s the approach taken by Cormier et al., where samples received different weights during training in such a way as to minimize churn. We have made a few deviations in our approach: (1) we are interested in reducing bad churn (churn involving new misclassifications) as opposed to all churn and (2) we would like to avoid the extreme memory requirements of the original method. In a similar manner to Cormier et al., we want to reduce the weight, e.g. importance, of previously misclassified samples during training of a new model. Practically, the model sees making the same mistakes as the previous model as cheaper than making a new mistake. Our weighing scheme gives all samples correctly classified by the original model a weight of one and all other samples have a weight of: w = α – β |0.5 – Pold (χi)|, where Pold (χi) is the output of the old model on sample χi and α, β are adjustable hyperparameters. We train this reduced churn operator model (RCOP) using an α of 0.9, a β of 0.6 and the same training data as the incremental model. RCOP produces 0.09% bad churn, 99.38% accuracy on test 2.

Results

Figure 3 shows both accuracy and bad churn of each model on test set 2. We compare the baseline model, the standard model retrain, the incrementally learned model and the RCOP model.

Figure 3: Bad churn versus accuracy on test set 2.

Table 1 summarizes each of these approaches, discussed in detail above.

Name

Trained on

Method

Total # of trees

Baseline

train

LightGBM

1000

Standard retrain

train + FPs/FNs from baseline on test 1

LightGBM

1100

Incremental model

train + FPs/FNs from baseline on test 1

Trained 100 new trees, starting from the baseline model

1100

RCOP

train + FPs/FNs from baseline on test 1

LightGBM with altered sample weights

1100

Table 1: A description of the models tested

The baseline model has 100 fewer trees than the other models, which could explain the comparatively reduced accuracy. However, we tried increasing the number of trees which resulted in only a minor increase in accuracy of < 0.001%. The increase in accuracy for the non-baseline methods is due to the differences in data set and training methods. Both incremental training and RCOP work as expected producing less churn than the standard retrain, while showing accuracy improvements over the baseline. In general, there is usually a trend of increasing accuracy being correlated with increasing bad churn: there is no free lunch. That increasing accuracy occurs due to changes in the decision boundary, the more improvement the more changes occur. It seems reasonable the increasing decision boundary changes correlate with an increase in bad churn although we see no theoretical justification for why that must always be the case.

Unexpectedly, both the incremental model and RCOP produce more accurate models with less churn than the standard retrain. We would have assumed that given their additional constraints both models would have less accuracy with less churn. The most direct comparison is RCOP versus the standard retrain. Both models use identical data sets and model parameters, varying only by the weights associated with each sample. RCOP reduces the weight of incorrectly classified samples by the baseline model. That reduction is responsible for the improvement in accuracy. A possible explanation of this behavior is mislabeled training data. Multiple authors have suggested identifying and removing points with label noise, often using the misclassifications of a previously trained model to identify those noisy points. Our scheme, which reduces the weight of those points instead of removing them, is not dissimilar to those other noise reduction approaches which could explain the accuracy improvement.

Conclusion

ML models experience an inherent struggle: not retraining means being vulnerable to new classes of threats, while retraining causes churn and potentially reintroduces old vulnerabilities. In this blog post, we have discussed two different approaches to modifying ML model training in order to reduce churn: incremental model training and churn-aware learning. Both demonstrate effectiveness in the EMBER malware classification data set by reducing the bad churn, while simultaneously improving accuracy. Finally, we also demonstrated the novel conclusion that reducing churn in a data set with label noise can result in a more accurate model. Overall, these approaches provide low technical debt solutions to updating models that allow data scientists and machine learning engineers to keep their models up-to-date against the latest cyber threats at minimal cost. At FireEye, our data scientists work closely with the FireEye Labs detection analysts to quickly identify misclassifications and use these techniques to reduce the impact of churn on our customers.

#gallery-0-5 { margin: auto; } #gallery-0-5 .gallery-item { float: left; margin-top: 10px; text-align: center; width: 33%; } #gallery-0-5 img { border: 2px solid #cfcfcf; } #gallery-0-5 .gallery-caption { margin-left: 0; } /* see gallery_shortcode() in wp-includes/media.php */

Go to Source Author: David Krisiloff Churning Out Machine Learning Models: Handling Changes in Model Predictions Original Post from FireEye Author: David Krisiloff Introduction Machine learning (ML) is playing an increasingly important role in…

[terabitweb]

Original Post from FireEye Author: David Krisiloff

Introduction

Machine learning (ML) is playing an increasingly important role in cyber security. Here at FireEye, we employ ML for a variety of tasks such as: antivirus, malicious PowerShell detection, and correlating threat actor behavior. While many people think that a data scientist’s job is finished when a model is built, the truth is that cyber threats constantly change and so must our models. The initial training is only the start of the process and ML model maintenance creates a large amount of technical debt. Google provides a helpful introduction to this topic in their paper “Machine Learning: The High-Interest Credit Card of Technical Debt.” A key concept from the paper is the principle of CACE: change anything, change everything. Because ML models deliberately find nonlinear dependencies between input data, small changes in our data can create cascading effects on model accuracy and downstream systems that consume those model predictions. This creates an inherent conflict in cyber security modeling: (1) we need to update models over time to adjust to current threats and (2) changing models can lead to unpredictable outcomes that we need to mitigate.

Ideally, when we update a model, the only change in model outputs are improvements, e.g. fixes to previous errors. Both false negatives (missing malicious activity) and false positives (alerts on benign activity), have significant impact and should be minimized. Since no ML model is perfect, we mitigate mistakes with orthogonal approaches: whitelists and blacklists, external intelligence feeds, rule-based systems, etc. Combining with other information also provides context for alerts that may not otherwise be present. However, CACE! These integrated systems can suffer unintended side effects from a model update. Even when the overall model accuracy has increased, individual changes in model output are not guaranteed to be improvements. Introduction of new false negatives or false positives in an updated model, called churn, creates the potential for new vulnerabilities and negative interactions with cyber security infrastructure that consumes model output. In this article, we discuss churn, how it creates technical debt when considering the larger cyber security product, and methods to reduce it.

Prediction Churn

Whenever we retrain our cyber security-focused ML models, we need to able to calculate and control for churn. Formally, prediction churn is defined as the expected percent difference between two different model predictions (note that prediction churn is not the same as customer churn, the loss of customers over time, which is the more common usage of the term in business analytics). It was originally defined by Cormier et al. for a variety of applications. For cyber security applications, we are often concerned with just those differences where the newer model performs worse than the older model. Let’s define bad churn when retraining a classifier as the percentage of misclassified samples in the test set which the original model correctly classified.

Churn is often a surprising and non-intuitive concept. After all, if the accuracy of our new model is better than the accuracy of our old model, what’s the problem? Consider the simple linear classification problem of malicious red squares and benign blue circles in Figure 1. The original model, A, makes three misclassifications while the newer model, B, makes only two errors. B is the more accurate model. Note, however, that B introduces a new mistake in the lower right corner, misclassifying a red square as benign. That square was correctly classified by model A and represents an instance of bad churn. Clearly, it’s possible to reduce the overall error rate while introducing a small number of new errors which did not exist in the older model.

Figure 1: Two linear classifiers with errors highlighted in orange. The original classifier A has lower accuracy than B. However, B introduces a new error in the bottom right corner.

Practically, churn introduces two problems in our models. First, bad churn may require changes to whitelist/blacklists used in conjunction with ML models. As we previously discussed, these are used to handle the small but inevitable number of incorrect classifications. Testing on large repositories of data is necessary to catch such changes and update associated whitelists and blacklists. Second, churn may create issues for other ML models or rule-based systems which rely on the output of the ML model. For example, consider a hypothetical system which evaluates URLs using both a ML model and a noisy blacklist. The system generates an alert if

P(URL = ‘malicious’) > 0.9 or

P(URL = ‘malicious’) > 0.5 and the URL is on the blacklist

After retraining, the distribution of P(URL=‘malicious’) changes and all .com domains receive a higher score. The alert rules may need to be readjusted to maintain the required overall accuracy of the combined system. Ultimately, finding ways of reducing churn minimizes this kind of technical debt.

Experimental Setup

We’re going to explore churn and churn reduction techniques using EMBER, an open source malware classification data set. It consists of 1.1 million PE files first seen in 2017, along with their labels and features. The objective is to classify the files as either goodware or malware. For our purposes we need to construct not one model, but two, in order to calculate the churn between models. We have split the data set into three pieces:

January through August is used as training data

September and October are used to simulate running the model in production and retraining (test 1 in Figure 2).

November and December are used to evaluate the models from step 1 and 2 (test 2 in Figure 2).

Figure 2: A comparison of our experimental setup versus the original EMBER data split. EMBER has a ten-month training set and a two-month test set. Our setup splits the data into three sets to simulate model training, then retraining while keeping an independent data set for final evaluation.

Figure 2 shows our data split and how it compares to the original EMBER data split. We have built a LightGBM classifier on the training data, which we’ll refer to as the baseline model. To simulate production testing, we run the baseline model on test 1 and record the FPs and FNs. Then, we retrain our model using both the training data and the FPs/FNs from test 1. We’ll refer to this model as the standard retrain. This is a reasonably realistic simulation of actual production data collection and model retraining. Finally, both the baseline model and the standard retrain are evaluated on test 2. The standard retrain has a higher accuracy than the baseline on test 2, 99.33% vs 99.10% respectively. However, there are 246 misclassifications made by the retrain model that were not made by the baseline or 0.12% bad churn.

Incremental Learning

Since our rationale for retraining is that cyber security threats change over time, e.g. concept drift, it’s a natural suggestion to use techniques like incremental learning to handle retraining. In incremental learning we take new data to learn new concepts without forgetting (all) previously learned concepts. That also suggests that an incrementally trained model may not have as much churn, as the concepts learned in the baseline model still exist in the new model. Not all ML models support incremental learning, but linear and logistic regression, neural networks, and some decision trees do. Other ML models can be modified to implement incremental learning. For our experiment, we incrementally trained the baseline LightGBM model by augmenting the training data with FPs and FNs from test 1 and then trained an additional 100 trees on top of the baseline model (for a total of 1,100 trees). Unlike the baseline model we use regularization (L2 parameter of 1.0); using no regularization resulted in overfitting to the new points. The incremental model has a bad churn of 0.05% (113 samples total) and 99.34% accuracy on test 2. Another interesting metric is the model’s performance on the new training data; how many of the baseline FPs and FNs from test 1 does the new model fix? The incrementally trained model correctly classifies 84% of the previous incorrect classifications. In a very broad sense, incrementally training on a previous model’s mistake provides a “patch” for the “bugs” of the old model.

Churn-Aware Learning

Incremental approaches only work if the features of the original and new model are identical. If new features are added, say to improve model accuracy, then alternative methods are required. If what we desire is both accuracy and low churn, then the most straightforward solution is to include both of these requirements when training. That’s the approach taken by Cormier et al., where samples received different weights during training in such a way as to minimize churn. We have made a few deviations in our approach: (1) we are interested in reducing bad churn (churn involving new misclassifications) as opposed to all churn and (2) we would like to avoid the extreme memory requirements of the original method. In a similar manner to Cormier et al., we want to reduce the weight, e.g. importance, of previously misclassified samples during training of a new model. Practically, the model sees making the same mistakes as the previous model as cheaper than making a new mistake. Our weighing scheme gives all samples correctly classified by the original model a weight of one and all other samples have a weight of: w = α – β |0.5 – Pold (χi)|, where Pold (χi) is the output of the old model on sample χi and α, β are adjustable hyperparameters. We train this reduced churn operator model (RCOP) using an α of 0.9, a β of 0.6 and the same training data as the incremental model. RCOP produces 0.09% bad churn, 99.38% accuracy on test 2.

Results

Figure 3 shows both accuracy and bad churn of each model on test set 2. We compare the baseline model, the standard model retrain, the incrementally learned model and the RCOP model.

Figure 3: Bad churn versus accuracy on test set 2.

Table 1 summarizes each of these approaches, discussed in detail above.

Name

Trained on

Method

Total # of trees

Baseline

train

LightGBM

1000

Standard retrain

train + FPs/FNs from baseline on test 1

LightGBM

1100

Incremental model

train + FPs/FNs from baseline on test 1

Trained 100 new trees, starting from the baseline model

1100

RCOP

train + FPs/FNs from baseline on test 1

LightGBM with altered sample weights

1100

Table 1: A description of the models tested

The baseline model has 100 fewer trees than the other models, which could explain the comparatively reduced accuracy. However, we tried increasing the number of trees which resulted in only a minor increase in accuracy of < 0.001%. The increase in accuracy for the non-baseline methods is due to the differences in data set and training methods. Both incremental training and RCOP work as expected producing less churn than the standard retrain, while showing accuracy improvements over the baseline. In general, there is usually a trend of increasing accuracy being correlated with increasing bad churn: there is no free lunch. That increasing accuracy occurs due to changes in the decision boundary, the more improvement the more changes occur. It seems reasonable the increasing decision boundary changes correlate with an increase in bad churn although we see no theoretical justification for why that must always be the case.

Unexpectedly, both the incremental model and RCOP produce more accurate models with less churn than the standard retrain. We would have assumed that given their additional constraints both models would have less accuracy with less churn. The most direct comparison is RCOP versus the standard retrain. Both models use identical data sets and model parameters, varying only by the weights associated with each sample. RCOP reduces the weight of incorrectly classified samples by the baseline model. That reduction is responsible for the improvement in accuracy. A possible explanation of this behavior is mislabeled training data. Multiple authors have suggested identifying and removing points with label noise, often using the misclassifications of a previously trained model to identify those noisy points. Our scheme, which reduces the weight of those points instead of removing them, is not dissimilar to those other noise reduction approaches which could explain the accuracy improvement.

Conclusion

ML models experience an inherent struggle: not retraining means being vulnerable to new classes of threats, while retraining causes churn and potentially reintroduces old vulnerabilities. In this blog post, we have discussed two different approaches to modifying ML model training in order to reduce churn: incremental model training and churn-aware learning. Both demonstrate effectiveness in the EMBER malware classification data set by reducing the bad churn, while simultaneously improving accuracy. Finally, we also demonstrated the novel conclusion that reducing churn in a data set with label noise can result in a more accurate model. Overall, these approaches provide low technical debt solutions to updating models that allow data scientists and machine learning engineers to keep their models up-to-date against the latest cyber threats at minimal cost. At FireEye, our data scientists work closely with the FireEye Labs detection analysts to quickly identify misclassifications and use these techniques to reduce the impact of churn on our customers.

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Go to Source Author: David Krisiloff Churning Out Machine Learning Models: Handling Changes in Model Predictions Original Post from FireEye Author: David Krisiloff Introduction Machine learning (ML) is playing an increasingly important role in…