Popular GitHub Repositories for Malware

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

Simple Linux post-exploitation agent that uses io_uring to stealthily bypass EDR detection by avoiding traditional syscalls.

[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 !

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

Malware Classification using Machine learning

红色团队技术集合

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

Adversarial Malware Generator Using GANs

Spot malwares using Machine Learning techniques

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

ExeShield AI detects malicious Windows executables using ML. Analyzes entropy, imports, and metadata for rapid classification, aiding incident response. Built with Python and scikit-learn.

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

Detecting malicious URLs using an autoencoder neural network