CYBER THREAT DETECTION IN INDUSTRIAL AUTOMATION

Abstract

The increasing adoption of industrial automation has revolutionized manufacturing and critical
infrastructure operations, enhancing efficiency and productivity. However, this digital transformation has also
made Industrial Automation and Control Systems (IACS) highly vulnerable to cyber threats, including malware
attacks, ransomware, and advanced persistent threats (APTs). Traditional security mechanisms often fail to
detect sophisticated cyber intrusions due to their dynamic and evolving nature. This paper presents a
comprehensive approach to cyber threat detection in industrial automation, leveraging advanced machine
learning techniques and deep learning models to enhance security. Specifically, we explore the application of an
Evolutionary Deep Belief Network (EDBN) to detect anomalies in industrial network traffic and identify
potential cyber threats in real time. Our model is designed to adapt to emerging threats by learning patterns
from historical attack data, ensuring robust and proactive defense mechanisms. Cyber threats targeting
industrial automation have grown in complexity and scale, with attackers exploiting vulnerabilities in industrial
networks, programmable logic controllers (PLCs), .Experimental results demonstrate that the proposed
approach significantly improves threat detection accuracy compared to conventional methods while
maintaining low false positive rates. Additionally, we explore the role of behavioral analysis, signature-based
detection, and hybrid methodologies in strengthening industrial cybersecurity. The study evaluates the
effectiveness of these approaches using benchmark datasets and real-world industrial scenarios, demonstrating
improved accuracy and reduced response time. The findings highlight the necessity of adaptive and intelligent
security solutions to safeguard industrial control systems from evolving cyber threats. Future research
directions include the incorporation of blockchain technology, federated learning, and autonomous threat
response mechanisms to further enhance security in industrial environments. The study underscores the
importance of AI-driven cybersecurity frameworks in safeguarding industrial automation systems and highlights
future research directions for strengthening cyber resilience in critical infrastructure