Intelligent AI-Powered Malware Evolution Detection and Classification Framework Using Explainable Machine Learning

Abstract

Intelligent AI-Powered Malware Evolution Detection and Classification Framework Using Explainable Machine Learning Malware is continuously evolving through polymorphism, metamorphism, obfuscation, packing, and other sophisticated techniques designed to evade conventional cybersecurity defenses. This book presents an AI-driven approach to understanding, detecting, classifying, and analyzing the evolution of modern malware. The book explores the integration of Machine Learning, Deep Learning, Explainable Artificial Intelligence (XAI), malware behavior analysis, feature engineering, and malware evolution detection. It explains how intelligent models can analyze static, dynamic, network, and behavioral characteristics to identify malicious software and classify malware families and variants. The book covers the fundamentals of malware and cyber threats, malware evolution, data collection and feature engineering, machine-learning-based classification, deep-learning approaches, and Explainable AI techniques such as LIME and SHAP. It also presents a proposed intelligent framework that combines malware detection, classification, similarity analysis, evolution tracking, explainability, and threat-intelligence generation. Key topics covered include: • Fundamentals of modern malware and cyber threats• Malware evolution, polymorphism, and metamorphism• Static, dynamic, and hybrid malware analysis• Malware datasets and feature engineering• Machine-learning algorithms for malware detection• Deep learning for intelligent malware analysis• CNN, RNN, LSTM, Autoencoder, and Transformer approaches• Explainable AI for malware classification• SHAP, LIME, and feature attribution techniques• Malware family and variant classification• Malware similarity and evolution analysis• Proposed AI-powered malware detection framework• Threat intelligence and security response• Model evaluation and performance analysis• Challenges and future directions in intelligent malware detection Designed for students, researchers, cybersecurity professionals, AI/ML practitioners, and academics, this book provides a structured foundation for understanding how Explainable Machine Learning can be integrated with intelligent malware analysis and evolving cyber-threat detection.