Popular GitHub Repositories for Malware Packers

A secure sandbox environment for malware developers and red teamers to test payloads against detection mechanisms before deployment. Integrates with LLM agents via MCP for enhanced analysis capabilities.

Open source project for data preparation of LLM application builders

Awesome graph anomaly detection techniques built based on deep learning frameworks. Collections of commonly used datasets, papers as well as implementations are listed in this github repository. We also invite researchers interested in anomaly detection, graph representation learning, and graph anomaly detection to join this project as contributors and boost further research in this area.

Machine Learning for Computer Security

Create adversarial attacks against machine learning Windows malware detectors

Machine Learning for Network Intrusion Detection & Misc Cyber Security Utilities

[IEEE S&P Workshop 2018] "Adversarial Deep Learning for Robust Detection of Binary Encoded Malware" Abdullah Al-Dujaili, Alex Huang, Erik Hemberg, Una-May O’Reilly

Malware Bypass Research using Reinforcement Learning

Malicious actors often reuse code to deploy their malware, phishing website or CNC server. As a result, similiaries can be found on URLs path by inspecting internet traffic. Moreover, deep learning models or even regular ML model do not fit for inline deployment in terms of running performance. However, regexes ( or YARA rules ) can be deployed on a proxy and work in real time on all the traffic. LUDA can take a set of malicious and benign URLs and return a list of regexes ready to be deployed inline !

Malware Classification using Machine learning

A Deep Learning framework that analyses Windows PE files to detect malicious Softwares.

Hrrformer: A Neuro-symbolic Self-attention Model (ICML23)

红色团队技术集合

Adversarial Malware Generator Using GANs

Spot malwares using Machine Learning techniques

Code for "MalGraph: Hierarchical Graph Neural Networks for Robust Windows Malware Detection"

Detecting malicious URLs using an autoencoder neural network

Detection of IoT devices infected by malwares from their network communications, using federated machine learning

Traffic analysis for Tor-based malware detection and classification

Android Malware Detection with Graph Convolutional Networks using Function Call Graph and its Derivatives.