Adversarial Machine Learning in Cybersecurity: A Survey of Attacks, Defenses, and Robustness Evaluation
TL;DR
The review explores the key adversarial attack classes: poisoning, evasion, model extraction, model extraction, model inversion, and membership inference and also white-box, black-box, and grey-box threat models.
Abstract
The rise of artificial intelligence (AI) and machine learning (ML) in cybersecurity has made Adversarial Machine Learning (AML) a key field of research. Though ML-based systems are more effective in intrusion detection, malware analysis, spam filtering and authentication, they are also susceptible to adversarial attacks that modify input samples, modify ML models or alter training data. The review explores the key adversarial attack classes: poisoning, evasion, model extraction, model inversion, and membership inference and also white-box, black-box, and grey-box threat models. It also provides an introduction to important defence methods like adversarial training, feature squeezing, defensive distillation, robust optimisation, detection-based methods, and ensemble learning. In addition, robustness evaluation metrics, benchmark datasets and attack assessment techniques to measure model robustness are highlighted. Lastly, the emerging trends are discussed in order to find future research directions in the field of creating trustworthy and resilient AI-based cybersecurity systems, such as Explainable AI, Federated Learning, Large Language Models, Autonomous Cyber defence, and Regulatory structures.