Popular GitHub Repositories for Malware Detection

Machine Learning for Computer Security

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

Spot malwares using Machine Learning techniques

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

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

ECE 188: Computer Security. Repository for "NLP-based Malware Detection on PDFs". Utilizing NLP techniques & transformer models to perform malware detection in PDFs.

This is a Malware Detection ML model made using Random Forest Algorithm

A beginner friendly repository for getting started with adversarial machine learning in PyTorch

Source code of Malware Classification by Learning Semantic and Structural Features of Control Flow Graphs (TrustCom 2021)

Python Project

Yossi Mandil & Tomer Gill's Bachelor Degree Final Project under the BIU Cyber Center - Malware & Benign File Classification using Machine Learning & Deep Learning

Exploring vulnerabilities of Transformers-based Malware Detectors to Adversarial Attacks

Generating Adversarial Malware Examples for White-Box Attacks Based on GAN

Open-MalSec 是一个开源数据集，专为网络安全研究和应用而创建。

This is the end to end Federated learning pipeline where multiple clients can train the model on decentralised data to obtain the robust model.This project works with any PE(Portable Executable) files to classify them as malware or legitimate.

Marmara üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisli?i b?lümünde sunulan “DER?N S?N?R A?LARI ?LE FEDERE ??RENME TABANLI B?R K?Tü AMA?LI YAZILIM TESP?T UYGULAMASI” ba?l?kl? tez ?al??mas?na ait kaynak kodlar?d?r.

This project is to build machine learning models on the byte and asm files to predict which type of malware these files represent. The byte files contain the hexadecimal codes and the asm file contains the assembly language code which contains keywords, opcodes, registers, APIs. We have to extract features from these files and build the optimal machine learning models on these extracted features.

An approach to classify hardware requests using a Stacked Denoising Autoencoder with TensorFlow

Malware Detection Using Machine Learning and Deep Learning Algorithms where highest Accuracy get 95%

An AI-driven MCP server that autonomously interfaces with MalwareBazaar, delivering real-time threat intel and sample metadata for authorized cybersecurity research workflows.