Adversarial Machine Learning in Cybersecurity: Threats, Mitigation, and Real-World Applications

Abstract

Adversarial machine learning (AML) represents a critical threat to cybersecurity systems that rely on artificial intelligence (AI) for intrusion detection, malware classification, and other tasks. This paper provides a comprehensive analysis of AML in the context of cybersecurity, exploring how malicious actors exploit machine learning (ML) vulnerabilities to compromise security systems. The growing sophistication of adversarial attacks threatens the reliability of AI models in real-world cybersecurity applications. This research also delves into mitigation strategies, including adversarial training, robust optimization, and secure data processing techniques. It explores the strengths and limitations of these techniques in real-world environments. Case studies illustrate the potential of AML attacks in disrupting AI-driven cybersecurity measures, and the paper concludes with future research directions aimed at securing ML systems from adversarial threats.