Malware Classification using Machine learning

Popular Malware-Samples for research and educational purposes.(60+ Samples!)

A large-scale database of malicious software images

Detecting malicious URLs using an autoencoder neural network

This GitHub repository contains an implementation of a malware classification/detection system using Convolutional Neural Networks (CNNs).

Malware Classification and Labelling using Deep Neural Networks

Few-Shot malware classification using fused features of static analysis and dynamic analysis （基于静态+动态分析的混合特征的小样本恶意代码分类框架）

Official implementation for the paper "On deceiving malware classification with section injection"

🪲 A list of malware and benign datasets for malware research

Training Vision Transformers from Scratch for Malware Classification

This tool clusters malware samples and extracts core shared artefacts by combining static analysis, optional dynamic analysis, and progressive comparison inside each cluster.

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

FewShot Malware Classification based on API call sequences, also as code repo for "A Novel Few-Shot Malware Classification Approach for Unknown Family Recognition with Multi-Prototype Modeling" paper.

Assessing 📊 the impact of class imbalance on model performance and convergence for malware byteplot image 🌌 classification

👾 Malware Classification using Deep Learning and Cuckoo Sandbox

Datawhale&科大讯飞2021A.I.开发者大赛恶意软件分类CV/NLP/表格三个方向的建模思路+伪标签LGB（rank11）

android-malware-classification using machine learning algorithms

Marmara Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği bölümünde sunulan “DERİN SİNİR AĞLARI İLE FEDERE ÖĞRENME TABANLI BİR KÖTÜ AMAÇLI YAZILIM TESPİT UYGULAMASI” başlıklı tez çalışmasına ait kaynak kodlarıdır.

Malware detector and classifier based on static analysis of PE executables

HGConv: Holographic Global Convolutional Networks

Malware Classification using the dataset provided by Microsoft

PyTorch dataset loader for image, text, malware, and medical classification datasets

in this project we used image processing Technique to classify 9 class malwares our final goal is to reach an appropriate model with high accuracy and small size and computational cost

No description provided.

A malware image dataset based on dynamic analysis and a classification model based on capsule network.

survey, detection & classification

Multimodal Deep Learning for Android Malware Classification (Mach. Learn. Knowl. Extr. 2025)

Malware

Classify malware into families based on file content and characteristics

The prevalence of IoT devices raises security concerns, as malware attacks can cause data breaches, privacy violations, and system failures. This report proposes a deep learning approach using Convolutional Neural Networks (CNNs) to detect malware in cross-architecture IoT devices. The model achieves 97% accuracy on a diverse IoT malware dataset.