A Comprehensive Review of Adversarial Machine Learning to Predict and Counter Evasive Malware

Abstract

This comprehensive review delves into applying adversarial machine-learning techniques for predicting and countering evasive malware. It begins by outlining the fundamental concepts of Adversarial Machine Learning (AML), including adversarial attacks such as evasion and poisoning, and their implications for cybersecurity. The review emphasizes how these attacks exploit vulnerabilities in machine learning models for malware detection, highlighting their challenges to conventional security systems. Key sections focus on the state-of-the-art adversarial training methods, which aim to enhance model robustness against such threats. We analyze various strategies to build more resilient detection systems, including advanced model architectures, data augmentation techniques, and defense mechanisms designed to detect and neutralize adversarial examples. The review also examines case studies and recent advancements in the field, evaluating the effectiveness of different approaches in real-world scenarios. The review identifies critical gaps and future research directions by synthesizing current research, providing a holistic view of how adversarial machine learning can be leveraged to improve malware defense. This analysis aims to equip researchers and practitioners with insights to develop more robust cybersecurity solutions capable of adapting to the evolving tactics of evasive malware.