Malware Detection and Classification Using Machine Learning

DrSemu - Sandboxed Malware Detection and Classification Tool Based on Dynamic Behavior

code for kaggle competition Microsoft malware classification

A malware analysis and classification tool.

Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine for Malware Classification

Kaggle微软恶意代码分类

This project seeks to apply machine learning algorithms to Android malware classification.

Kaggle 'Microsoft Malware Classification Challenge' 3rd place solution

The current repository contains all the scripts needed to reproduce the results published in the paper: "Obfuscation Revealed: Electromagnetic obfuscated malware classification".

Malware detection project on Android devices using machine learning classification algorithms.

Malware Classification using Machine learning

Multi-class malware classification using Deep Learning

Transfer Learning for Image-Based Malware Classification

Using GreyWolfOptimization for feature selection and multi kernel SVM for classification for Malware Hunting on IoT devices

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

Traffic analysis for Tor-based malware detection and classification

使用Bert做embedding，结合BiLSTM做恶意软件的多分类任务

Malware Classification and Labelling using Deep Neural Networks

本科毕业设计_恶意代码检测分类平台

Data and code for malware classification using machine learning (for fun, not production)

[code] "CFGExplainer: Explaining Graph Neural Network-Based Malware Classification from Control Flow Graphs" by Jerome Dinal Herath, Priti Prabhakar Wakodikar, Ping Yang and Guanhua Yan. In: IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) (2022)

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"

🔍 "2015 Microsoft Malware Classification Challenge" - Using machine learning to classify malware into different families based on Windows PE structures, disassembly scripts and machine code.（“2015微软恶意软件分类挑战赛” - 使用机器学习基于Windows PE结构、反汇编和机器代码对恶意软件进行分类。）

FANCI is a prototype implementation of a machine learning based classification engine for non-existent domains to detect domain gernation algorithm malware traffic.

Training Vision Transformers from Scratch for Malware Classification

Beating the benchmark for Microsoft Malware Classification Challenge (BIG 2015)

A novel and interpretable ML-based approach to classify malware with high accuracy and explain the classification result meanwhile.

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