AI-Driven Threat Detection and Resilience in Cyber–Physical Systems

Dear Colleagues,

Cyber–Physical Systems (CPSs) are becoming increasingly intelligent and complex due to the integration of artificial intelligence (AI), Internet of Things (IoT), and distributed computing architectures. On the one hand, this increases their efficiency and functionality, but on the other hand, it exposes them to new, sophisticated, and difficult-to-detect security threats. Modern attacks on CPSs often use targeted, long-term methods (advanced persistent threats), machine learning techniques, or vulnerabilities at the interface of the physical and digital worlds.

The aim of this Special Issue is to collate the latest research and reviews on AI-based methods (including machine learning/deep learning) in the context of

• Anomaly detection;
• Risk assessment;
• Resilience engineering;
• Predictive security;
• Self-organizing and adaptive defense mechanisms.

We invite the submission of original and review papers focused on the following areas:

• Intelligent threat detection mechanisms in CPSs and IoT;
• Using AI/ML/DL to protect industrial infrastructure and SCADA;
• Cyber resilience and autonomous responses to threats;
• CPS security in the automotive, energy, medicine, and robotics domains;
• Edge AI and security in distributed architectures;
• Formal methods and hybrid models for secure CPS design;
• Explainable AI in the context of critical systems security;
• Integration of cybersecurity and functional security (cyber–physical safety–security co-design).

Our goal is to create a platform for interdisciplinary knowledge exchange, connecting experts in the fields of security, artificial intelligence, automation, systems engineering, and industrial informatics.

Dr. Jakub Szyguła
Dr. Dariusz Marek
Dr. Krzysztof Bernacki
Dr. Grzegorz Tytko
Guest Editors

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