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Advanced Security and Privacy Mechanisms for Cyber–Physical Systems and Industrial...
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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 2712

Special Issue Editor

Prof. Dr. Jiuxin Cao

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Southeast University, Sipailou, Nanjing 210096, China
Interests: information processing; ensemble contrastive learning; action recognition; neural networks

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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Published Papers (4 papers)

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Research

25 pages, 1214 KB  
Article
Towards Realistic Industrial Anomaly Detection: MADE-Net Framework and ManuDefect-21 Benchmark
by Junyang Yang, Jiuxin Cao and Chengge Duan
Appl. Sci. 2025, 15(20), 10885; https://doi.org/10.3390/app152010885 - 10 Oct 2025
Viewed by 367
Abstract
Visual anomaly detection (VAD) plays a critical role in manufacturing and quality inspection, where the scarcity of anomalous samples poses challenges for developing reliable models. Existing approaches primarily rely on unsupervised training with synthetic anomalies, which often favor specific defect types and struggle [...] Read more.
Visual anomaly detection (VAD) plays a critical role in manufacturing and quality inspection, where the scarcity of anomalous samples poses challenges for developing reliable models. Existing approaches primarily rely on unsupervised training with synthetic anomalies, which often favor specific defect types and struggle to generalize across diverse categories. To address these limitations, we propose MADE-Net (Multi-model Adaptive anomaly Detection Ensemble Network), an industrial anomaly detection framework that integrates three complementary submodels: a reconstruction-based submodel (SRAD), a feature embedding-based submodel (SFAD), and a patch discrimination submodel (LPD). A dynamic integration and selection module (ISM) adaptively determines the most suitable submodel output according to input characteristics. We further introduce ManuDefect-21, a large-scale benchmark dataset comprising 11 categories of electronic components with both normal and anomalous samples in the training and test sets. The dataset reflects realistic positive-to-negative ratios and diverse defect types encountered in real manufacturing environments, addressing several limitations of previous datasets such as MVTec-AD and VisA. Experiments conducted on ManuDefect-21 demonstrate that MADE-Net achieves consistent improvements in both detection and localization metrics (e.g., average AUROC of 98.5%, Pixel-AP of 68.7%) compared with existing methods. While MADE-Net requires pixel-level annotations for fine-tuning and introduces additional computational overhead, it provides enhanced adaptability to complex industrial conditions. The proposed framework and dataset jointly contribute to advancing practical and reproducible research in industrial anomaly detection. Full article
(This article belongs to the Special Issue Advanced Security and Privacy Mechanisms for Cyber–Physical Systems and Industrial Networks)
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20 pages, 939 KB  
Article
Dynamic Defense Strategy Selection Through Reinforcement Learning in Heterogeneous Redundancy Systems for Critical Data Protection
by Xuewen Yu, Lei He, Jingbu Geng, Zhihao Liang, Zhou Gan and Hantao Zhao
Appl. Sci. 2025, 15(16), 9111; https://doi.org/10.3390/app15169111 - 19 Aug 2025
Viewed by 586
Abstract
In recent years, the evolution of cyber-attacks has exposed critical vulnerabilities in conventional defense mechanisms, particularly across national infrastructure systems such as power, transportation, and finance. Attackers are increasingly deploying persistent and sophisticated techniques to exfiltrate or manipulate sensitive data, surpassing static defense [...] Read more.
In recent years, the evolution of cyber-attacks has exposed critical vulnerabilities in conventional defense mechanisms, particularly across national infrastructure systems such as power, transportation, and finance. Attackers are increasingly deploying persistent and sophisticated techniques to exfiltrate or manipulate sensitive data, surpassing static defense methods that depend on known vulnerabilities. This growing threat landscape underscores the urgent need for more advanced and adaptive defensive strategies to counter continuously evolving attack vectors. To address this challenge, this paper proposes a novel reinforcement learning-based optimization framework integrated with a Dynamic Heterogeneous Redundancy (DHR) architecture. Our approach uniquely utilizes reinforcement learning for the dynamic scheduling of encryption-layer configurations within the DHR framework, enabling adaptive adjustment of defense policies based on system status and threat progression. We evaluate the proposed system in a simulated adversarial environment, where reinforcement learning continuously adjusts encryption strategies and defense behaviors in response to evolving attack patterns and operational dynamics. Experimental results demonstrate that our method achieves a higher defense success rate while maintaining lower defense costs, thereby enhancing system resilience against cyber threats and improving the efficiency of defensive resource allocation. Full article
(This article belongs to the Special Issue Advanced Security and Privacy Mechanisms for Cyber–Physical Systems and Industrial Networks)
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27 pages, 8383 KB  
Article
A Resilience Quantitative Assessment Framework for Cyber–Physical Systems: Mathematical Modeling and Simulation
by Zhigang Cao, Hantao Zhao, Yunfan Wang, Chuan He, Ding Zhou and Xiaopeng Han
Appl. Sci. 2025, 15(15), 8285; https://doi.org/10.3390/app15158285 - 25 Jul 2025
Viewed by 727
Abstract
As cyber threats continue to grow in complexity and persistence, resilience has become a critical requirement for cyber–physical systems (CPSs). Resilience quantitative assessment is essential for supporting secure system design and ensuring reliable operation. Although various methods have been proposed for evaluating CPS [...] Read more.
As cyber threats continue to grow in complexity and persistence, resilience has become a critical requirement for cyber–physical systems (CPSs). Resilience quantitative assessment is essential for supporting secure system design and ensuring reliable operation. Although various methods have been proposed for evaluating CPS resilience, major challenges remain in accurately modeling the interaction between cyber and physical domains and in providing structured guidance for resilience-oriented design. This study proposes an integrated CPS resilience assessment framework that combines cyber-layer anomaly modeling based on Markov chains with mathematical modeling of performance degradation and recovery in the physical domain. The framework establishes a structured evaluation process through parameter normalization and cyber–physical coupling, enabling the generation of resilience curves that clearly represent system performance changes under adverse conditions. A case study involving an industrial controller equipped with a diversity-redundancy architecture is conducted to demonstrate the applicability of the proposed method. Modeling and simulation results indicate that the framework effectively reveals key resilience characteristics and supports performance-informed design optimization. Full article
(This article belongs to the Special Issue Advanced Security and Privacy Mechanisms for Cyber–Physical Systems and Industrial Networks)
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30 pages, 1042 KB  
Article
A Privacy-Preserving Polymorphic Heterogeneous Security Architecture for Cloud–Edge Collaboration Industrial Control Systems
by Yukun Niu, Xiaopeng Han, Chuan He, Yunfan Wang, Zhigang Cao and Ding Zhou
Appl. Sci. 2025, 15(14), 8032; https://doi.org/10.3390/app15148032 - 18 Jul 2025
Viewed by 664
Abstract
Cloud–edge collaboration industrial control systems (ICSs) face critical security and privacy challenges that existing dynamic heterogeneous redundancy (DHR) architectures inadequately address due to two fundamental limitations: event-triggered scheduling approaches that amplify common-mode escape impacts in resource-constrained environments, and insufficient privacy-preserving arbitration mechanisms for [...] Read more.
Cloud–edge collaboration industrial control systems (ICSs) face critical security and privacy challenges that existing dynamic heterogeneous redundancy (DHR) architectures inadequately address due to two fundamental limitations: event-triggered scheduling approaches that amplify common-mode escape impacts in resource-constrained environments, and insufficient privacy-preserving arbitration mechanisms for sensitive industrial data processing. In contrast to existing work that treats scheduling and privacy as separate concerns, this paper proposes a unified polymorphic heterogeneous security architecture that integrates hybrid event–time triggered scheduling with adaptive privacy-preserving arbitration, specifically designed to address the unique challenges of cloud–edge collaboration ICSs where both security resilience and privacy preservation are paramount requirements. The architecture introduces three key innovations: (1) a hybrid event–time triggered scheduling algorithm with credibility assessment and heterogeneity metrics to mitigate common-mode escape scenarios, (2) an adaptive privacy budget allocation mechanism that balances privacy protection effectiveness with system availability based on attack activity levels, and (3) a unified framework that organically integrates privacy-preserving arbitration with heterogeneous redundancy management. Comprehensive evaluations using natural gas pipeline pressure control and smart grid voltage control systems demonstrate superior performance: the proposed method achieves 100% system availability compared to 62.57% for static redundancy and 86.53% for moving target defense, maintains 99.98% availability even under common-mode attacks (10−2 probability), and consistently outperforms moving target defense methods integrated with state-of-the-art detection mechanisms (99.7790% and 99.6735% average availability when false data deviations from true values are 5% and 3%, respectively) across different attack detection scenarios, validating its effectiveness in defending against availability attacks and privacy leakage threats in cloud–edge collaboration environments. Full article
(This article belongs to the Special Issue Advanced Security and Privacy Mechanisms for Cyber–Physical Systems and Industrial Networks)
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