Adaptation of adversarial machine learning for training agents to counter data attacks

Abstract

Adversarial Machine Learning (AML) has emerged as a critical field of study, focusing on enhancing the robustness of machine learning models against data attacks. This article explores the adaptation of AML techniques to train intelligent agents capable of countering various attack types, including data poisoning and evasion. We discuss the theoretical foundations of AML, prevalent attack vectors, and methodologies for agent training. Our findings demonstrate that integrating adversarial training with reinforcement learning significantly improves model resilience, ensuring the security of machine learning applications. The proposed approach is validated through case studies in cybersecurity, autonomous systems, and finance. Experiments show that AML- trained agents achieve up to 92 % attack detection accuracy, reducing risks in autonomous systems by 40 %.