On adversarial attack detection in the artificial intelligence era: Fundamentals, a taxonomy, and a review

Abstract

The rapid advancement and sophisticated deployment of artificial intelligence tools by malicious actors have led to the rise of highly complex cyber-attacks that evolve quickly. This rapid evolution has made traditional defense systems increasingly ineffective at detecting and mitigating these hidden threats. Adversarial attacks are a prime example of such sophisticated cyber-attacks; they subtly alter attack patterns to evade detection by intelligent systems while still maintaining their harmful functionality. This paper provides a comprehensive overview of computer malware, examining both traditional concealment methods and more advanced adversarial techniques. It includes an in-depth analysis of recent research efforts aimed at detecting previously unseen adversarial attacks using both traditional and AI-driven approaches. Furthermore, this study discusses the limitations of current network intrusion detection systems and proposes directions for future research.