Popular GitHub Repositories for Malware Defense

FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai

Must-read Papers on Textual Adversarial Attack and Defense

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.

Chinese safety prompts for evaluating and improving the safety of LLMs. 中文安全prompts，用于评估和提升大模型的安全性。

A pytorch adversarial library for attack and defense methods on images and graphs

Sentiment Analysis, Text Classification, Text Augmentation, Text Adversarial defense, etc.;

Open source project for data preparation of LLM application builders

Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. arXiv:2408.07666.

Adversarial attacks and defenses on Graph Neural Networks.

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.

? Adversarial attacks on explanations and how to defend them

auto_LiRPA: An Automatic Linear Relaxation based Perturbation Analysis Library for Neural Networks and General Computational Graphs

Implementation of the KDD 2020 paper "Graph Structure Learning for Robust Graph Neural Networks"

Machine Learning for Computer Security

Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models (published in ICLR2018)

Create adversarial attacks against machine learning Windows malware detectors

Code for our NeurIPS 2019 *spotlight* "Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers"

A certifiable defense against adversarial examples by training neural networks to be provably robust

A curated list of papers on adversarial machine learning (adversarial examples and defense methods).

Machine Learning for Network Intrusion Detection & Misc Cyber Security Utilities