hack-model-35

[hack-model-35]

A Brief History of Hackerdom: The Early Hackers

💾 ►►► DOWNLOAD FILE 🔥🔥🔥 The term describes a research field regarding the study and design of adversarial attacks targeting Artificial Intelligence AI models and features. Even this simple definition can send the most knowledgeable security practitioner running! Within AI, the model — a mathematical algorithm that provides insights to enable business results — can be attacked without knowledge of the actual model created. Features are those characteristics of a model that define the output desired. Features can also be attacked without knowledge of the features used! In the feedback learning loop of AI, recurrent training of the model occurs in order to comprehend new threats and keep the model current see Figure 1. With model hacking, the attacker can poison the Training Set. Figure 2 shows a rockhopper penguin targeted digitally. A white-box evasion example we knew the model and the features , a few pixel changes and the poor penguin in now classified as a frying pan or a computer with excellent accuracy. While most current model hacking research focuses on image recognition, we have investigated evasion attacks and mitigation methods for malware detection and static analysis. Utilizing malware samples highlighting FakeInstaller, and k benign samples and 5. This, of course, is a concern to all of us. Adversarial examples are inputs to ML models that an attacker has intentionally designed to cause the model to make a mistake [1]. The JSMA algorithm needs only a minimum number of features need to be modified. Obviously, that can be catastrophic! Demonstrating this theory of transferability in Figure 5, the attacker constructed a source or substitute model of a K-Nearest Neighbor KNN algorithm, creating adversarial examples, which targeted a Support Vector Machine SVM algorithm. It resulted in an However, using a substitute model and transferring the adversarial examples to the victim i. Another catastrophic example! While malware represents the most common artifact deployed by cybercriminals to attack victims, numerous other targets exist that pose equal or perhaps even greater threats. Over the last 18 months, we have studied what has increasingly become an industry research trend: digital and physical attacks on traffic signs. Research in this area dates back several years and has since been replicated and enhanced in numerous publications. We initially set out to reproduce one of the original papers on the topic, and built a highly robust classifier, using an RGB Red Green Blue webcam to classify stop signs from the LISA [6] traffic sign data set. The model performed exceptionally well, handling lighting, viewing angles, and sign obstruction. Over a period of several months, we developed model hacking code to cause both untargeted and targeted attacks on the sign, in both the digital and physical realms. Following on this success, we extended the attack vector to speed limit signs, recognizing that modern vehicles increasingly implement camera-based speed limit sign detection, not just as input to the Heads-Up-Display HUD on the vehicle, but in some cases, as input to the actual driving policy of the vehicle. Ultimately, we discovered that minuscule modifications to speed limit signs could allow an attacker to influence the autonomous driving features of the vehicle, controlling the speed of the adaptive cruise control! For more detail on this research, please refer to our extensive blog post on the topic. The good news is that much like classic software vulnerabilities, model hacking is possible to defend against, and the industry is taking advantage of this rare opportunity to address the threat before it becomes of real value to the adversary. Detecting and protecting against model hacking continues to develop with many articles published weekly. Detection methods include ensuring that all software patches have been installed, closely monitoring drift of False Positives and False Negatives, noting cause and effect of having to change thresholds, retraining frequently, and auditing decay in the field i. In addition, human-machine teaming is critical to ensure that machines are not working autonomously and have oversight from humans-in-the-loop. Machines currently do not understand context; however, humans do and can consider all possible root causes and mitigations of a nearly imperceptible shift in metrics. There are pros and cons of each method, and the reader is encouraged to consider their specific ecosystem and security metrics to select the appropriate method. While there has been no documented report of model hacking in the wild yet , it is notable to see the increase of research over the past few years: from less than 50 literature articles in to over in It is also notable that, perhaps for the first time in cybersecurity, a body of researchers have proactively developed the attack, detection, and protection against these unique vulnerabilities. We will continue to add to the greater body of knowledge of model hacking attacks as well as ensure the solutions we implement have built-in detection and protection. Our research excels in targeting the latest algorithms, such as GANS Generative Adversarial Networks in malware detection, facial recognition, and image libraries. We are also in process of transferring traffic sign model hacking to further real-world examples. Lastly, we believe McAfee leads the security industry in this critical area. Each leverages its unique skillsets; ATR with in-depth and leading-edge security research capabilities, and AAT, through its world-class data analytics and artificial intelligence expertise. When combined, these teams are able to do something few can; predict, research, analyze and defend against threats in an emerging attack vector with unique components, before malicious actors have even begun to understand or weaponize the threat. Follow us to stay updated on all things McAfee and on top of the latest consumer and mobile security threats. Steve Povolny is the Head of Advanced Threat Research for McAfee Enterprise, which delivers groundbreaking vulnerability research spanning nearly every industry. With more than a decade of experience in network Podcast Safety Tips. Introduction and Application of Model Hacking. Feb 19, Stay Updated Follow us to stay updated on all things McAfee and on top of the latest consumer and mobile security threats. Steve Povolny. Their goal The Emotet maldoc was