International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025
p-ISSN: 2395-0072
www.irjet.net
Hybrid Anomaly Detection in OT System Updates: A Comparative Analysis of Isolation Forest and Autoencoders Dr. Lenny Michael Bonnes Colorado Technical University, Colorado Springs. Colorado ---------------------------------------------------------------------***--------------------------------------------------------------------
Abstract - Operational Technology (OT) environments
detection in OT software updating and patching processes is a critical mechanism for identifying irregular patterns or behaviors that deviate from expected norms. These anomalies may include failed patch deployments, unexpected system behavior post-update, unauthorized changes, or potential cybersecurity threats. Detecting such anomalies early is essential for maintaining the reliability, security, and operational efficiency of OT systems. The ability to identify and mitigate these irregularities before they cause significant disruptions is crucial in preventing downtime and ensuring the resilience of industrial control environments.
are critical infrastructures that support industrial control systems, power grids, transportation networks, and manufacturing operations. Ensuring the security and reliability of software updates and patching processes in OT is a complex challenge due to the stringent real-time requirements, legacy system constraints, and high availability demands. Anomalies during the update and patching process can lead to system failures, security breaches, and operational disruptions. This paper explores the application of machine learning-based anomaly detection techniques, specifically Isolation Forest and Autoencoders, to enhance the security and resilience of OT system updates. By leveraging these techniques, organizations can proactively detect unusual patterns in patch deployment, identify potential software integrity issues, and mitigate risks associated with failed updates. The study provides a comparative analysis of these anomaly detection methods and their effectiveness in securing OT environments against unexpected software failures and cyber threats.
The primary objective of this paper is to explore the application of anomaly detection techniques in the updating and patching of OT systems. Specifically, it examines the effectiveness of machine learning-based approaches such as Isolation Forests and Autoencoders in identifying anomalies during software updates. The paper provides a detailed analysis of these methods, their relevance in OT environments, and how they can be integrated into existing security and monitoring frameworks. Additionally, it highlights challenges, best practices, and real-world considerations for implementing anomaly detection in OT system maintenance. Through this exploration, the study contributes to enhancing the security and reliability of OT patching processes, supporting the continued evolution of industrial cybersecurity practices.
Key Words: Anomaly Detection, Isolation Forest, OT Security, Patching, Machine Learning, AI-Driven Monitoring, Autoencoders, Industrial Control Systems, System Updates, Cyber Resilience
1. INTRODUCTION The landscape of industrial operations has been profoundly transformed by the increasing integration of digital technologies in Operational Technology (OT) environments. At its core, OT systems control critical infrastructure, including power grids, transportation networks, and manufacturing processes, making them essential for operational continuity and safety. The maintenance and security of these systems rely heavily on regular software updates and patching to address vulnerabilities, improve performance, and ensure compliance with industry regulations. However, updating and patching OT systems present unique challenges due to their real-time requirements, legacy hardware dependencies, and the need for high availability. A failed or improperly executed update can disrupt operations, compromise safety, or introduce security vulnerabilities.
1.1 Analyzing Anomaly Detection Techniques in OT System Updates and Patching Anomaly detection plays a crucial role in ensuring the integrity and security of software updates and patching processes within Operational Technology (OT) environments. Given the critical nature of these systems, detecting irregularities in update deployments can prevent operational disruptions, security vulnerabilities, and potential failures. Two advanced machine learning techniques, Isolation Forests and Autoencoders, offer unique approaches to identifying anomalies in OT system updates and patching. Isolation Forest for OT System Updates Isolation Forest is an anomaly detection algorithm designed to isolate outliers rather than model normal data
Within this dynamic environment, the importance of anomaly detection cannot be overstated. Anomaly
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