Securing Industrial Control Systems: Components, Cyber Threats, and Machine Learning-Driven Defense Strategies

Industrial Control Systems (ICS), which include Supervisory Control and Data Acquisition (SCADA) systems, Distributed Control Systems (DCS), and Programmable Logic Controllers (PLC), play a crucial role in managing and regulating industrial processes. However, ensuring the security of these systems...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 23; číslo 21; s. 8840
Hlavní autoři: Nankya, Mary, Chataut, Robin, Akl, Robert
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 30.10.2023
Témata:
anomaly detection
Automation
Communication
Connectivity
Control algorithms
Controllers
cyber defense
cyber threats
Cyberterrorism
industrial control systems
Interfaces
Internet
Machine learning
Nuclear energy
Physical instruments
SCADA
Security management
Sensors
Software
Structured Query Language-SQL
Telemetry
vulnerabilities
Wide area networks
security
cyber defense
attacks
industrial control systems
vulnerabilities
anomaly detection
cyber threats
artificial intelligence
SCADA
ISSN:1424-8220, 1424-8220
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Shrnutí:Industrial Control Systems (ICS), which include Supervisory Control and Data Acquisition (SCADA) systems, Distributed Control Systems (DCS), and Programmable Logic Controllers (PLC), play a crucial role in managing and regulating industrial processes. However, ensuring the security of these systems is of utmost importance due to the potentially severe consequences of cyber attacks. This article presents an overview of ICS security, covering its components, protocols, industrial applications, and performance aspects. It also highlights the typical threats and vulnerabilities faced by these systems. Moreover, the article identifies key factors that influence the design decisions concerning control, communication, reliability, and redundancy properties of ICS, as these are critical in determining the security needs of the system. The article outlines existing security countermeasures, including network segmentation, access control, patch management, and security monitoring. Furthermore, the article explores the integration of machine learning techniques to enhance the cybersecurity of ICS. Machine learning offers several advantages, such as anomaly detection, threat intelligence analysis, and predictive maintenance. However, combining machine learning with other security measures is essential to establish a comprehensive defense strategy for ICS. The article also addresses the challenges associated with existing measures and provides recommendations for improving ICS security. This paper becomes a valuable reference for researchers aiming to make meaningful contributions within the constantly evolving ICS domain by providing an in-depth examination of the present state, challenges, and potential future advancements.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23218840