malware-detection-project

Android Malware Detection Using Machine Learning Project with Source Code and Documents Plus Video Explanation

Malware🦠 Detection and Analysis using Machine Learning (MDAML) is designed to provide users with an intuitive interface for analyzing and detecting malware in various file formats.

Multi-layered malware detection system using static analysis, dynamic browser automation, and external APIs for accurate website threat identification. Project Code, Documents and Video Implementation

Real-time malware scanning using ClamAV. Watches folders, scans new or changed files, quarantines anything suspicious, and runs on boot through systemd.

Image-based malware classification using CNN, ResNet18, and EfficientNet-B0 trained on the Malimg Dataset. Includes model comparison, evaluation metrics, and visualization of results.

A Python-based ransomware detection and prevention tool that monitors file system activity for suspicious behavior patterns.

CalibGuard：低成本文件安全审计系统（本地校准 CodeBERT 门控 + 云端 LLM 按需复核）

CalibGuard 文件安全审计的 DeepSeek Harness 插件：本地校准 CodeBERT 门控 + 云端复核，以 agent 工具与侧边栏面板两种形态使用

This repository houses documentation and reports for a variety of malware analysis cases, insights into different threats and their behaviors

Projects Based On Machine Learning With Source Code. Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials

Malicious Link Detector – Advanced protection against suspicious links and malware

Real-time malware detection system using hybrid ML (Random Forest + LightGBM + MLP) and static file analysis, with auto-quarantine and a live Flask dashboard.