Adversarial Machine Learning in Industrial Cybersecurity
Binastya Anggara Sekti · Advances in computational intelligence and robotics book series · 2025
As industrial systems increasingly integrate machine learning (ML) for cybersecurity, they face a growing threat from adversarial machine learning (AML) attacks. AML techniques, such as evasion, poisoning, and model extraction, exploit vulnerabilities in ML models to manipulate security defenses, leading to misclassifications, false positives, and system failures. These threats pose severe risks to industrial environments, including operational disruptions, financial losses, and compromised safety. We will explores the challenges of AML in industrial cybersecurity, focusing on the lack of robustness in ML models, limited availability of high-quality industrial datasets, high computational costs, and the evolving nature of adversarial attacks. Additionally, future research directions, such as secure federated learning and AI-driven attack response mechanisms, are discussed. By strengthening ML-based security frameworks, industrial organizations can enhance resilience against adversarial threats and protect critical infrastructure from evolving cyber risks.