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malware-detection-project

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Android-Malware-Detection-Using-Machine-Learning
Malware-Detection-and-Analysis-using-Machine-Learning

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.

  • Updated Feb 4, 2026
  • Jupyter Notebook
Malware-Scanner-System

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

  • Updated Jun 3, 2025

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

  • Updated Nov 3, 2025
  • Jupyter Notebook

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.

  • Updated Jun 16, 2026
  • Python
Project-Based-On-Machine-Learning-With-Source-Code

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