Artificial intelligence and industrial cyber‐physical security [Guest Editorial]

Saxena, Neetesh ORCID: https://orcid.org/0000-0002-6437-0807, Choi, Bong Jun, Choo, Kim‐Kwang Raymond and Dehghantanha, Ali 2026. Artificial intelligence and industrial cyber‐physical security [Guest Editorial]. IET Cyber-Physical Systems: Theory & Applications 11 (1) , e70052. 10.1049/cps2.70052

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Abstract

Cyber‐Physical Systems (CPS) are central to modern smart infrastructures, enabling intelligent processes that handle large volumes of data while ensuring security, safety, reliability, and resilience. The integration of Artificial Intelligence (AI), particularly deep learning, with CPS and Internet of Things (IoT) technologies is driving advancements in Industry 4.0, smart grids, and intelligent transportation systems. AI enhances cybersecurity by enabling anomaly detection, continuous monitoring, network scanning, log analysis, and classification of data into legitimate and malicious categories, allowing real‐time threat identification and proactive mitigation. At the same time, the adoption of CPS and Industrial Internet of Things (IIoT) introduces significant cybersecurity challenges, as security incidents can directly affect humans and critical assets. This convergence of AI and cybersecurity provides a dual advantage: improving operational efficiency while reinforcing defences against evolving cyber‐physical threats. By enabling real‐time threat detection, resilient response, and robust protection of both infrastructure and AI algorithms, CPS can maintain safety, reliability, and performance under complex cyber and physical stressors. This special issue highlights research at the intersection of AI, cybersecurity, and CPS/IIoT, focusing on innovations that enhance resilience, protect critical systems, and support the sustainable growth of smart industrial and urban ecosystems.

Item Type:	Short Communication
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/
Publisher:	Wiley
ISSN:	2398-3396
Date of First Compliant Deposit:	28 May 2026
Date of Acceptance:	20 April 2026
Last Modified:	28 May 2026 09:00
URI:	https://orca.cardiff.ac.uk/id/eprint/187248

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