An advanced deep learning solution for detecting IoT botnet attacks, specifically Mirai, leveraging the power of GNNs. This project transforms raw IoT device network traffic into dynamic graph structures and employs a GraphSAGE model for robust and efficient anomaly detection, enhancing the security posture of interconnected devices.

A comprehensive deep learning framework for phishing detection, utilizing Graph Neural Networks (GraphSAGE) to analyze interconnected web features. Features include temporal graph construction, causal learning for robust time-series analysis, and integrated noise injection testing to evaluate model resilience against data imperfections.

This project focuses on the classification of malware based on system process behavior. It utilizes machine learning techniques to analyze features extracted from running processes to distinguish between benign and malicious software. The goal is to develop an effective and interpretable model for real-time malware detection. Tags (for GitHub):