Android Malware Detection Using Machine Learning Project with Source Code and Documents Plus Video Explanation
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Updated
Dec 25, 2022 - Jupyter Notebook
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
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.
A security-focused wrapper around yay that scans AUR packages before installation for suspicious PKGBUILD behavior, supply-chain risks, obfuscation, unsafe downloads, install hooks, and package impersonation.
Projects Based On Machine Learning With Source Code. Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials
This repository houses documentation and reports for a variety of malware analysis cases, insights into different threats and their behaviors
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