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):

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.