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learning-code-ficusoft · 5 months ago
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Deploying Containers on AWS ECS with Fargate
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Introduction
Amazon Elastic Container Service (ECS) with AWS Fargate enables developers to deploy and manage containers without managing the underlying infrastructure. Fargate eliminates the need to provision or scale EC2 instances, providing a serverless approach to containerized applications.
This guide walks through deploying a containerized application on AWS ECS with Fargate using AWS CLI, Terraform, or the AWS Management Console.
1. Understanding AWS ECS and Fargate
✅ What is AWS ECS?
Amazon ECS (Elastic Container Service) is a fully managed container orchestration service that allows running Docker containers on AWS.
✅ What is AWS Fargate?
AWS Fargate is a serverless compute engine for ECS that removes the need to manage EC2 instances, providing:
Automatic scaling
Per-second billing
Enhanced security (isolation at the task level)
Reduced operational overhead
✅ Why Choose ECS with Fargate?
✔ No need to manage EC2 instances ✔ Pay only for the resources your containers consume ✔ Simplified networking and security ✔ Seamless integration with AWS services (CloudWatch, IAM, ALB)
2. Prerequisites
Before deploying, ensure you have:
AWS Account with permissions for ECS, Fargate, IAM, and VPC
AWS CLI installed and configured
Docker installed to build container images
An existing ECR (Elastic Container Registry) repository
3. Steps to Deploy Containers on AWS ECS with Fargate
Step 1: Create a Dockerized Application
First, create a simple Dockerfile for a Node.js or Python application.
Example: Node.js DockerfiledockerfileFROM node:16-alpine WORKDIR /app COPY package.json . RUN npm install COPY . . CMD ["node", "server.js"] EXPOSE 3000
Build and push the image to AWS ECR:shaws ecr create-repository --repository-name my-app docker build -t my-app . docker tag my-app:latest <AWS_ACCOUNT_ID>.dkr.ecr.<REGION>.amazonaws.com/my-app:latest aws ecr get-login-password --region <REGION> | docker login --username AWS --password-stdin <AWS_ACCOUNT_ID>.dkr.ecr.<REGION>.amazonaws.com docker push <AWS_ACCOUNT_ID>.dkr.ecr.<REGION>.amazonaws.com/my-app:latest
Step 2: Create an ECS Cluster
Use the AWS CLI to create a cluster:shaws ecs create-cluster --cluster-name my-cluster
Or use Terraform:hclresource "aws_ecs_cluster" "my_cluster" { name = "my-cluster" }
Step 3: Define a Task Definition for Fargate
The task definition specifies how the container runs.
Create a task-definition.js{ "family": "my-task", "networkMode": "awsvpc", "executionRoleArn": "arn:aws:iam::<AWS_ACCOUNT_ID>:role/ecsTaskExecutionRole", "cpu": "512", "memory": "1024", "requiresCompatibilities": ["FARGATE"], "containerDefinitions": [ { "name": "my-container", "image": "<AWS_ACCOUNT_ID>.dkr.ecr.<REGION>.amazonaws.com/my-app:latest", "portMappings": [{"containerPort": 3000, "hostPort": 3000}], "essential": true } ] }
Register the task definition:shaws ecs register-task-definition --cli-input-json file://task-definition.json
Step 4: Create an ECS Service
Use AWS CLI:shaws ecs create-service --cluster my-cluster --service-name my-service --task-definition my-task --desired-count 1 --launch-type FARGATE --network-configuration "awsvpcConfiguration={subnets=[subnet-xyz],securityGroups=[sg-xyz],assignPublicIp=\"ENABLED\"}"
Or Terraform:hclresource "aws_ecs_service" "my_service" { name = "my-service" cluster = aws_ecs_cluster.my_cluster.id task_definition = aws_ecs_task_definition.my_task.arn desired_count = 1 launch_type = "FARGATE" network_configuration { subnets = ["subnet-xyz"] security_groups = ["sg-xyz"] assign_public_ip = true } }
Step 5: Configure a Load Balancer (Optional)
If the service needs internet access, configure an Application Load Balancer (ALB).
Create an ALB in your VPC.
Add an ECS service to the target group.
Configure a listener rule for routing traffic.
4. Monitoring & Scaling
🔹 Monitor ECS Service
Use AWS CloudWatch to monitor logs and performance.shaws logs describe-log-groups
🔹 Auto Scaling ECS Tasks
Configure an Auto Scaling Policy:sh aws application-autoscaling register-scalable-target \ --service-namespace ecs \ --scalable-dimension ecs:service:DesiredCount \ --resource-id service/my-cluster/my-service \ --min-capacity 1 \ --max-capacity 5
5. Cleaning Up Resources
After testing, clean up resources to avoid unnecessary charges.shaws ecs delete-service --cluster my-cluster --service my-service --force aws ecs delete-cluster --cluster my-cluster aws ecr delete-repository --repository-name my-app --force
Conclusion
AWS ECS with Fargate simplifies container deployment by eliminating the need to manage servers. By following this guide, you can deploy scalable, cost-efficient, and secure applications using serverless containers.
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govindhtech · 1 year ago
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FakeNet-NG: Powerful Malware Analysis and Network Simulation
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What is FakeNet-NG?
A dynamic network analysis tool called FakeNet-NG mimics network services and records network requests to help in malware research. The FLARE team is dedicated to improving and maintaining the tool to increase its functionality and usability. Though highly configurable and platform-neutral, FakeNet needed a more user-friendly and intuitive presentation of the network data it collected so you could find pertinent Network-Based Indicators (NBIs) more quickly. Google expanded FakeNet-NG to provide HTML-based output that allows you to examine, explore, and share collected network data in order to solve this problem and further improve usability.
In order to overcome this difficulty and improve usability even further, they expanded the functionality of FakeNet-NG to provide HTML-based output, which lets you see, investigate, and share network data that has been gathered.
Engaging HTML-Based Results
An HTML page with inline CSS and Javascript supports the new interactive output of FakeNet-NG. FakeNet-NG’s current text-based output and the new HTML-based output. Using a Jinja2 template that it fills with the network data it has collected, FakeNet-NG creates each report. Your preferred browser can be used to view the final report once it has been saved to the current working directory. To analyse the recorded network activity together, you may also distribute this file to other people.
Captured network data can be chosen, filtered, and copied using the HTML interface.
Network data that has been gathered can be chosen, filtered, and copied using the HTML interface.
Creation and Execution
Planning and Execution
Insides of FakeNet-NG
FakeNet-NG Tutorial
The three main components that make up FakeNet-NG’s modular architecture are as follows:
Diverter: The target system’s main component intercepts all incoming and outgoing network traffic. It sends these packets to the Proxy Listener by default so that it can process them further.
Between the Diverter and the protocol-specific Listeners lies a component known as the Proxy Listener. Based on variables including port, protocol, and data content, it examines application layer data to determine which Listener is best for every network packet.
Protocol-specific Listeners: These specialized Listeners process requests unique to their particular protocols and produce responses that resemble authentic server behavior. Examples of these specialized Listeners are HTTP, FTP, and DNS.
Extending NBI Analysis Using FakeNet-NG
It was necessary to enhance essential components in order to record, store, and associate network data with the source activities in order to enable FakeNet-NG to provide thorough and informative reports.
FakeNet-NG Comprised:
Improving data storage: The Diverter keeps track of extra data, such as process IDs, names, and linkages between source ports that were started by the proxy and those that were started by the original.
Presenting NBI mapping: The Diverter allows for the unambiguous attribution of network activity by mapping network data to source processes.
Encouraging information exchange: To ensure precise data monitoring, the Proxy Listener sends pertinent packet metadata to the Diverter.
The interactive HTML-based output is created by combining the data that is captured by each component using FakeNet-NG
NetworkMode: Choose the network mode that FakeNet-NG should operate in.
NetworkMode: Choose which network mode to use while launching FakeNet-NG.
Acceptable configurations.
Suitable configurations.
SingleHost: control traffic coming from nearby processes.
Manipulate traffic from other systems with MultiHost.
Auto: Select the NetworkMode that works best for the platform right now.
Presently, not every platform supports every NetworkMode configuration.
This is how support is currently standing:
Only Windows supports OneHost
With the exception of process, port, and host blacklisting and whitelisting, Linux supports both MultiHost and, in an experimental state, SingleHost mode.
To access Linux’s MultiHost mode and Windows’ SingleHost mode, leave this set to Auto for the time being.
DNS-related setting and Windows implementation:
ModifyLocalDNS – direct the local machine’s DNS service to the DNS listener of FakeNet-NG.
Cease DNS Service: This command ends the DNS client service (Dnscache) on Windows. In contrast to the standard’svchost.exe’ process, this enables FakeNet-NG to observe the real processes that resolve domains.
Linux version
The following settings are supported by the Linux version of Diverter:
LinuxRedirectNonlocal – This tells you which externally facing network interfaces to reroute to FakeNet-NG when you use it to mimic Internet connectivity for a separate host.
Before adding rules for FakeNet-NG, use LinuxFlushIptables to flush all iptables rules.
As long as the Linux Diverter’s termination sequence remains unbroken, the previous rules will be reinstated.
LinuxFlushDnsCommand: If necessary, enter the appropriate command here to clear the DNS resolver cache for your Linux distribution.
Select which detailed debug events to show with the DebugLevel option.
Upcoming projects
However think there is still room to improve the HTML-based output from FakeNet-NG so that analysts can benefit even more. A communication graph, network behavior graphically would be a crucial contribution. With edges connecting process nodes to other nodes like IP addresses or domain names, this widely used approach maps processes to the corresponding network requests. You might quickly and easily grasp a program’s communication patterns by using FakeNet-NG with this kind of visualization.
Get rid of unnecessary network traffic: Reduce noise produced by safe Windows services and apps so that the most important network information is highlighted.
Make sure the HTML report includes ICMP traffic: Present a more thorough overview of network activity by showcasing network data based on ICMP.
Add pre-set filters and filtering options: Provide pre-set filters and easy-to-use filtering tools to omit typical Microsoft network traffic.
Enhance the usability of exported network data by giving the user the option to select the information that should be included in the exported Markdown data. This will improve the formatting of the Markdown data.
In conclusion
As the go-to tool for dynamic network analysis in malware research, FakeNet-NG keeps getting better. It intend to improve its usefulness by providing interactive HTML-based output, which will enable you to traverse and analyses even the largest and most intricate network data grabs in a clear, simple, and aesthetically pleasing manner.
To make your analysis of dynamic network data more efficient, they invite you to investigate the new HTML-based output and take advantage of its filtering, selection, and copying features. For the most recent version of FakeNet-NG, download it from our Github repository, make contributions to the project, or leave comments.
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renovatio06 · 3 years ago
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Research triggers revision of a leading theory of consciousness | Big Think
Research triggers revision of a leading theory of consciousness | Big Think
Source: Research triggers revision of a leading theory of consciousness – Big Think Sounds interesting. Largely adopted consciousness theories are being put to the test again. https://bigthink.com/neuropsych/revision-leading-theory-consciousness/ artist’s rendition of activated neural pathways in transparent head depiction Credit: agsandrew / Adobe Stock
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blackmedals · 5 years ago
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robertbryantblog · 6 years ago
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terabitweb · 6 years ago
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Original Post from FireEye Author: Michael Bailey
Introduction
In 2016, FLARE introduced FakeNet-NG, an open-source network analysis tool written in Python. FakeNet-NG allows security analysts to observe and interact with network applications using standard or custom protocols on a single Windows host, which is especially useful for malware analysis and reverse engineering. Since FakeNet-NG’s release, FLARE has added support for additional protocols. FakeNet-NG now has out-of-the-box support for DNS, HTTP (including BITS), FTP, TFTP, IRC, SMTP, POP, TCP, and UDP as well as SSL.
Building on this work, FLARE has now brought FakeNet-NG to Linux. This allows analysts to perform basic dynamic analysis either on a single Linux host or using a separate, dedicated machine in the same way as INetSim. INetSim has made amazing contributions to the productivity of the security community and is still the tool of choice for many analysts. Now, FakeNet-NG gives analysts a cross-platform tool for malware analysis that can directly integrate with all the great Python-based infosec tools that continually emerge in the field.
Getting and Installing FakeNet-NG on Linux
If you are running REMnux, then good news: REMnux now comes with FakeNet-NG installed, and existing users can get it by running the update-remnux command.
For other Linux distributions, setting up and using FakeNet-NG will require the Python pip package manager, the net-tools package, and the development files for OpenSSL, libffi, and libnetfilterqueue. Here is how to quickly obtain the appropriate prerequisites for a few common Linux distributions:
Debian and Ubuntu: sudo apt-get install python-pip python-dev libssl-dev libffi-dev libnetfilter-queue-dev net-tools
Fedora 25 and CentOS 7: 
yum -y update;
yum -y install epel-release; # <-- If CentOS
yum -y install redhat-rpm-config; # <-- If Fedora
yum -y groupinstall ‘Development Tools’; yum -y install python-pip python-devel openssl-devel libffi-devel libnetfilter_queue-devel net-tools
Once you have the prerequisites, you can download the latest version of FakeNet-NG and install it using setup.py install.
A Tale of Two Modes
On Linux, FakeNet-NG can be deployed in MultiHost mode on a separate host dedicated to network simulation, or in the experimental SingleHost mode for analyzing software locally. Windows only supports SingleHost mode. FakeNet-NG is configured by default to run in NetworkMode: Auto, which will automatically select SingleHost mode on Windows or MultiHost mode on Linux. Table 1 lists the currently supported NetworkMode settings by operating system.
  SingleHost
MultiHost
Windows
Default (Auto)
Unsupported
Linux
Experimental
Default (Auto)
Table 1: FakeNet-NG NetworkMode support per platform
FakeNet-NG’s support for SingleHost mode on Linux currently has limitations.
First, FakeNet-NG does not yet support conditional redirection of specific processes, hosts, or ports on Linux. This means that settings like ProcessWhiteList will not work as expected. We plan to add support for these settings in a later release. In the meantime, SingleHost mode supports redirecting all Internet-bound traffic to local listeners, which is the main use case for malware analysts.
Second, the python-netfilterqueue library is hard-coded to handle datagrams of no more than 4,012 octets in length. Loopback interfaces are commonly configured with high maximum transmittal unit (MTU) settings that allow certain applications to exceed this hard-coded limit, resulting in unanticipated network behavior. An example of a network application that may exhibit issues due to this would be a large file transfer via FTP. A workaround is to recompile python-netfilterqueue with a larger buffer size or to decrease the MTU for the loopback interface (i.e. lo) to 4,012 or less.
Configuring FakeNet-NG on Linux
In addition to the new NetworkMode setting, Linux support for FakeNet-NG introduces the following Linux-specific configuration items:
LinuxRedirectNonlocal: For MultiHost mode, this setting specifies a comma-delimited list of network interfaces for which to redirect all traffic to the local host so that FakeNet-NG can reply to it. The setting in FakeNet-NG’s default configuration is *, which configures FakeNet-NG to redirect on all interfaces.
LinuxFlushIptables: Deletes all iptables rules before adding rules for FakeNet-NG. The original rules are restored as part of FakeNet-NG’s shutdown sequence which is triggered when you hit Ctrl+C. This reduces the likelihood of conflicting, erroneous, or duplicate rules in the event of unexpected termination, and is enabled in FakeNet-NG’s default configuration.
LinuxFlushDnsCommand: Specifies the command to flush the DNS resolver cache. When using FakeNet-NG in SingleHost mode on Linux, this ensures that name resolution requests are forwarded to a DNS service such as the FakeNet-NG DNS listener instead of using cached answers. The setting is not applicable on all distributions of Linux, but is populated by default with the correct command for Ubuntu Linux. Refer to your distribution’s documentation for the proper command for this behavior.
Starting FakeNet-NG on Linux
Before using FakeNet-NG, also be sure to disable any services that may bind to ports corresponding to the FakeNet-NG listeners you plan to use. An example is Ubuntu’s use of a local dnsmasq service. You can use netstat to find such services and should refer to your Linux distribution’s documentation to determine how to disable them.
You can start FakeNet-NG by invoking fakenet with root privileges, as shown in Figure 1.
Figure 1: Starting FakeNet-NG on Linux
You can alter FakeNet-NG’s configuration by either directly editing the file displayed in the first line of FakeNet-NG’s output, or by creating a copy and specifying its location with the -c command-line option.
Conclusion
FakeNet-NG now brings the convenience of a modern, Python-based, malware-oriented network simulation tool to Linux, supporting the full complement of listeners that are available on FakeNet-NG for Windows. Users of REMnux can make use of FakeNet-NG already, while users of other Linux distributions can download and install it using standard package management tools.
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Go to Source Author: Michael Bailey Introducing Linux Support for FakeNet-NG: FLARE’s Next Generation Dynamic Network Analysis Tool Original Post from FireEye Author: Michael Bailey Introduction In 2016, FLARE introduced FakeNet-NG, an open-source network analysis tool written in…
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blackmedals · 5 years ago
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blackmedals · 6 years ago
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robertbryantblog · 6 years ago
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blackmedals · 6 years ago
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Kini ready di Indonesia Android81 Tablet Ten Core 10.1" Rp. 1.285.000 Specification: Name:8G+128GB Android 8.1 Tablet PC Quad Core HD WIFI Bluetooth 2 SIM 4G 10.1" ShellMaterial:Plastic CPU:mtk6797,Eight coree,clocked at 2 Ghz Operating system:Android8.1 Operating Memory(RAM):8G Bodymemory:128G Flash(the actually capacity of less than this value), because the mobile phone software space) Screensize:10.1 inches Screenresolution:2560*1600 Networkmode: Support 4G Network/GSM/WCDMA/TD-SCDMA (Not support telecom card) Maximum storage expansion:MicroSD(TF)64G Support SIM card quantity:dual card dual standby Touch screen:capacitive screen,multi-touch ScreenType:IPS Camera:rear 1300 millionpixels(withflash),front 800 millionpixels Camerasensortype:CMOS Audioformat:supportMP3,WMA,APE,FLAC,OGG,WAVandsoon Videoformat:support3gp,AVI,MP4,MKV,F4Vandsoon Imageformat:supportJPEG,PNG,BMP,GIFandsoon MIC:Built-inMIC I/Ointerface:a3.5mm headphone jack,a Micro USB interface,aTF card slot,2 SIM card slot OTGfunction:support GPS Navigation Function:support WIFI:WirelessWIFI Bluetooth version:4.0 Built in applications:Browser,Email,VideoPlayer,Calculator,Calendar,Clock,FileManager,Camera,Music. Language:Chinese,SimplifiedChinese,Japanese,Korean,English,German,French,Italian,Russian,etc. Battery:8800mAh lithium battery(built-in) Life:3-4 hours of life,music 10 hours Package Content: 1 x Tablet PC 1 x Charger 1 x USB cable 1 x Manual https://www.instagram.com/p/B4IPCUMpth9/?igshid=1cbjch7xkpwpj
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