AI-Based Anomaly Detection for Industrial Cybersecurity Applications

Andrej Šimunek

AI-Based Anomaly Detection for Industrial Cybersecurity Applications

Author(s)	Andrej Šimunek
Country	Slovakia
Abstract	The rapid digitalization of industrial environments has increased the dependence of manufacturing, energy, transportation, water, and process-control infrastructures on interconnected cyber-physical systems. While connectivity improves automation, operational visibility, and production efficiency, it also introduces security vulnerabilities that can be exploited through malware, unauthorized access, protocol manipulation, insider activity, and sophisticated network attacks. Conventional signature-based security mechanisms may be insufficient for identifying previously unseen or behaviorally complex threats. Artificial intelligence (AI)-based anomaly detection provides an alternative approach by learning patterns of normal system behavior and identifying deviations that may indicate malicious or abnormal activity. This paper examines the application of AI-based anomaly detection techniques to industrial cybersecurity environments. The study focuses on machine learning, deep learning, behavioral analytics, feature engineering, and intelligent classification approaches for detecting anomalous network and system activities. A structured analytical methodology is used to evaluate these technologies according to detection capability, adaptability, computational requirements, interpretability, and operational suitability. The analysis demonstrates that AI-based approaches can enhance the detection of unknown attacks, abnormal industrial communication, unusual device behavior, and deviations from established operational patterns. However, their effectiveness depends strongly on the quality of training data, feature representation, model selection, and continuous adaptation to changing industrial conditions. The study further identifies challenges associated with false positives, concept drift, adversarial manipulation, limited labeled datasets, legacy equipment, and real-time processing requirements. An integrated cybersecurity architecture combining AI-based anomaly detection with network segmentation, industrial firewalls, identity management, and security monitoring can provide stronger protection than isolated AI systems. Future research should investigate explainable AI, federated learning, digital twins, adaptive learning, and autonomous cyber defense mechanisms for industrial environments. The paper concludes that AI-based anomaly detection can become an important component of modern industrial cybersecurity when implemented within a multilayered, risk-aware, and operationally compatible security framework.
Keywords	Artificial Intelligence, Anomaly Detection, Industrial Cybersecurity, Machine Learning, Deep Learning, Industrial Control Systems, Cyber-Physical Systems, Behavioral Analytics.
Field	Engineering
Published In	Volume 3, Issue 8, August 2026
Published On	2026-08-17

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AI-Based Anomaly Detection for Industrial Cybersecurity Applications

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