Adversarial Machine Learning Defenses in AI-Enabled Cybersecurity Systems

This paper explores the effectiveness of adversarial machine learning (AML) defense strategies in enhancing the resilience of AI-enabled cybersecurity systems against sophisticated adversarial attacks. With the rapid adoption of AI in security-critical domains, ensuring model robustness has become paramount, particularly in the face of threats such as gradient-based and query-based adversarial perturbations. The study evaluates five widely recognized defense mechanisms—adversarial training, defensive distillation, gradient masking, ensemble learning, and input preprocessing—across key performance metrics including accuracy, precision, recall, F1-score, and robustness. Experimental results demonstrate that while each defense offers varying degrees of protection, ensemble learning consistently outperforms others, achieving the highest robustness and detection performance. The findings reveal that no single method can provide complete immunity, but strategic combinations and layered defenses offer substantial improvements in adversarial resistance. This research contributes to the understanding of AML defenses, guiding the development of more secure and dependable AI-driven cybersecurity systems.