Advanced Security and Privacy Mechanisms for Cyber–Physical Systems and Industrial Networks
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 November 2025 | Viewed by 26
Special Issue Editor
Special Issue Information
Dear Colleagues,
The rapid evolution of cyberspace, including advancements in computer networks, CPS industry systems, social networks, and hybrid networks, is driven by the IoT, 5G, and cloud infrastructures. However, it has also presented vulnerabilities to various threats such as stealthy cyberattacks, adversarial manipulations, and privacy breaches. This Special Issue encourages the submission of cutting-edge research on data-driven anomaly detection, chaos-driven cryptography, and privacy-preserving technologies tailored to modern networks. We seek contributions that advance deep learning, statistical models to detect emerging anomalies such as adversarial attacks, zero-day exploits, and behavioral deviations in dynamic network environments. We also welcome studies that highlight innovative mechanisms, including federated learning, homomorphic encryption, differential privacy, and blockchain-based solutions, to ensure user or system privacy. Topics include, but are not limited to, the following:
- Privacy-preserving techniques for cyber–physical systems;
- Chaos-based cryptography for cyber–physical systems;
- Anomaly detection in industry sites;
- Generative content detection for cyber–physical systems;
- Privacy–utility trade-offs in network security;
- Intrusion detection and prevention in industrial control systems;
- Network anomalous traffic detection.
Prof. Dr. Jiuxin Cao
Guest Editor
Manuscript Submission Information
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Keywords
- anomaly detection
- privacy–utility trade-offs
- chaos-based encryption
- federated learning
- network resilience
- unknown threat detection
- generative content forensics
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