AI-Driven Threat Detection and Resilience in Cyber–Physical Systems
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 25 March 2026 | Viewed by 23
Special Issue Editors
Interests: long-range dependence (LRD); self-similarity; traffic sources; Hurst parameter; Markov models; neural networks; convolutional neural networks; Hurst exponent; internet traffic; fractional Gaussian noise; water consumption; IoT; cloud computing; smart home; interactive shower panel; Industry 4.0; active queue management; diffusion approximation; fractional controller PIγ; internet; TCP/IP and UDP; congestion control; dropping packets; PIα controller; adaptive AQM; self similarity; PID; reinforcement learning
Special Issues, Collections and Topics in MDPI journals
Interests: neural networks; convolutional neural networks; Hurst exponent; self-similarity; internet traffic; fractional Gaussian noise; SDN switch; quality of service (QoS); diffusion approximation; water consumption; IoT; cloud computing; smart home; interactive shower panel; Industry 4.0; active queue management; fractional controller PIγ; internet; TCP/IP and UDP; congestion control; dropping packets
Special Issues, Collections and Topics in MDPI journals
Interests: power electronics; electromagnetic compatibility; control system; properties of magnetic materials; power conversion systems; precision signal measurement; design of energy-efficient systems; data acquisition system
Special Issues, Collections and Topics in MDPI journals
Interests: eddy current testing; electromagnetism; impedance analysis; analytical modeling
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
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
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- cyber–physical systems (CPSs)
- artificial intelligence (AI)
- Internet of Things (IoT)
- machine learning/deep learning
- attack detection and prevention
- real-time monitoring
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