IEEE Transactions on Industrial Cyber-Physical Systems最新文献_第2页

Open-Set Fault Diagnosis for Industrial Rotating Machines Based on Trustworthy Deep Learning 基于可信赖深度学习的工业旋转机械开集故障诊断

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2025-02-11 DOI: 10.1109/TICPS.2025.3539997

Dongdong Wei;Mingjian Zuo;Zhigang Tian

引用次数: 0

Safety-Aware Scheduling of Real-Time Control Systems With Burst Computing Tasks 具有突发计算任务的实时控制系统的安全感知调度

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2025-02-04 DOI: 10.1109/TICPS.2025.3537961

Ting Cheng;Yonghui Liang;Hui Li;Qimin Xu;Shanying Zhu

{"title":"Safety-Aware Scheduling of Real-Time Control Systems With Burst Computing Tasks","authors":"Ting Cheng;Yonghui Liang;Hui Li;Qimin Xu;Shanying Zhu","doi":"10.1109/TICPS.2025.3537961","DOIUrl":"https://doi.org/10.1109/TICPS.2025.3537961","url":null,"abstract":"This paper investigates the safety-aware task scheduling problem of real-time control systems in the presence of burst computing tasks. The basic idea is to adaptively release resources from low-criticality control tasks, ensuring timely completion of burst computing tasks and keeping the control system state within a safe threshold. An efficient algorithm called ADP-Opt is proposed to solve the problem. It first decouples the complex original problem into multiple subproblems, which greatly reduces the search space and improves the solution efficiency. In the subproblem, safety is maintained by limiting the upper bound of the system state's deviation to below the threshold. By introducing a penalty term in the cost function related to computing resources, we aim to balance the impact of control computing tasks on control performance and the consumption of computing resources. Consequently, the overall cost of the control system can be minimized while releasing enough resources for burst computing tasks. Simulation results show that the scheduling decisions made by ADP-Opt approach the optimal decision and maintain the safety of the control systems.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"3 ","pages":"218-227"},"PeriodicalIF":0.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed Online Optimization Of Consensus-Based Adaptive Gradients Over Time-Varying Networks 时变网络中基于共识的自适应梯度的分布式在线优化

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2025-02-04 DOI: 10.1109/TICPS.2025.3538690

Qingyang Sheng;Xiasheng Shi;Yanxu Su

引用次数: 0

Industrial Rare Cyber Attack Detection Based on Federated Diffusion-Squeeze Graph Modeling 基于联邦扩散-挤压图模型的工业稀有网络攻击检测

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2025-01-24 DOI: 10.1109/TICPS.2025.3533461

Fangyu Li;Junnuo Lin;Di Wang;Hongyan Yang

{"title":"Industrial Rare Cyber Attack Detection Based on Federated Diffusion-Squeeze Graph Modeling","authors":"Fangyu Li;Junnuo Lin;Di Wang;Hongyan Yang","doi":"10.1109/TICPS.2025.3533461","DOIUrl":"https://doi.org/10.1109/TICPS.2025.3533461","url":null,"abstract":"Distributed learning applied in industrial cyber-physical systems (ICPS) is vulnerable to cyber attacks, especially rare ones. Common data-driven cyber attack detection approaches face the challenges of imbalanced data, resulting in insufficient extraction of anomalous features. To enhance the sensitivity of rare cyber attack detection in complex ICPS, we propose a federated diffusion-squeeze graph model (FedDSG). In each edge device, we construct a local diffusion-based generative module to balance rare anomalous data and construct feature graphs, which maintains information fidelity and type balance of data. To alleviate the extra computational load, we establish a graph-structured detection module based on information bottleneck (IB) to filter out redundant topological features and identify the optimal graph for modeling. In the central server, we design an aggregation strategy in the central server to consolidate a global FedDSG and the global generative module generates synthetic cyber attack data to retrain the global detection module. In addition, we verify FedDSG using public industrial datasets on the self-constructed simulation platform. The results show that FedDSG improves the efficiency of rare cyber attack detection.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"3 ","pages":"150-164"},"PeriodicalIF":0.0,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-Based Q-Learning for Replay Attack Detection in Cyber-Physical Systems 基于数据的q -学习网络物理系统重放攻击检测

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2025-01-23 DOI: 10.1109/TICPS.2025.3532881

Junshuai Qin;Zhengdao Zhang;Huarong Zhao

引用次数: 0

Optimal Transmission Power Scheduling for Networked Control System Under DoS Attack DoS攻击下网络控制系统的最优传输功率调度

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2025-01-16 DOI: 10.1109/TICPS.2025.3530406

Siyi Wang;Yulong Gao;Sandra Hirche

{"title":"Optimal Transmission Power Scheduling for Networked Control System Under DoS Attack","authors":"Siyi Wang;Yulong Gao;Sandra Hirche","doi":"10.1109/TICPS.2025.3530406","DOIUrl":"https://doi.org/10.1109/TICPS.2025.3530406","url":null,"abstract":"Designing networked control systems that are reliable and resilient against adversarial threats, is essential for ensuring the security of cyber-physical systems. This paper addresses the communication-control co-design problem for networked control systems under denial-of-service (DoS) attacks. In wireless channels, a transmission power scheduler periodically determines the power level for sensory data transmission. Yet DoS attacks render data packets unavailable by disrupting the communication channel. This paper co-designs the control and power scheduling laws in the presence of DoS attacks and aims to minimize the sum of regulation control performance and transmission power consumption. Both finite- and infinite-horizon discounted cost criteria are addressed. By delving into the information structure between the controller and the power scheduler under attack, the original co-design problem is divided into two subproblems that can be solved individually without compromising optimality. The optimal control is shown to be certainty equivalent, and the optimal transmission power scheduling is solved using a dynamic programming approach. Moreover, in the infinite-horizon scenario, we analyze the performance of the designed scheduling policy and develop an upper bound of the total costs. Finally, a numerical example is provided to demonstrate the theoretical results.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"3 ","pages":"198-207"},"PeriodicalIF":0.0,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

2024 Index IEEE Transactions on Industrial Cyber-Physical Systems Vol. 2 IEEE工业信息物理系统学报第2卷

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2025-01-16 DOI: 10.1109/TICPS.2025.3530736

引用次数: 0

Sustainable Diffusion-Based Incentive Mechanism for Generative AI-Driven Digital Twins in Industrial Cyber-Physical Systems 工业信息物理系统中生成式人工智能驱动数字孪生的可持续扩散激励机制

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-12-31 DOI: 10.1109/TICPS.2024.3524483

Jinbo Wen;Jiawen Kang;Dusit Niyato;Yang Zhang;Shiwen Mao

{"title":"Sustainable Diffusion-Based Incentive Mechanism for Generative AI-Driven Digital Twins in Industrial Cyber-Physical Systems","authors":"Jinbo Wen;Jiawen Kang;Dusit Niyato;Yang Zhang;Shiwen Mao","doi":"10.1109/TICPS.2024.3524483","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3524483","url":null,"abstract":"Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries. By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures. Thanks to data process capability, Generative Artificial Intelligence (GenAI) can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing. However, mechanisms that leverage Industrial Internet of Things (IIoT) devices to share sensing data for DT construction are susceptible to adverse selection problems. In this paper, we first develop a GenAI-driven DT architecture in ICPSs. To address the adverse selection problem caused by information asymmetry, we propose a contract theory model and develop a sustainable diffusion-based soft actor-critic algorithm to identify the optimal feasible contract. Specifically, we leverage dynamic structured pruning techniques to reduce parameter numbers of actor networks, allowing sustainability and efficient implementation of the proposed algorithm. Numerical results demonstrate the effectiveness of the proposed scheme and the algorithm, enabling efficient DT construction and updates to monitor and manage ICPSs.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"3 ","pages":"139-149"},"PeriodicalIF":0.0,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unsupervised Real-Time Communication Traffic Anomaly Detection for Multi-Dimensional Industrial Networks 多维工业网络的无监督实时通信流量异常检测

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-12-30 DOI: 10.1109/TICPS.2024.3524185

Weijie hao;Zebang Zhang;Xuan Wang;Qiang Yang;Bo Liu;Wenhai Wang;Tao Yang;Peng Ye

{"title":"Unsupervised Real-Time Communication Traffic Anomaly Detection for Multi-Dimensional Industrial Networks","authors":"Weijie hao;Zebang Zhang;Xuan Wang;Qiang Yang;Bo Liu;Wenhai Wang;Tao Yang;Peng Ye","doi":"10.1109/TICPS.2024.3524185","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3524185","url":null,"abstract":"Security risks exist in various dimensions of Industrial Cyber-Physical Systems(ICPS), and network traffic analysis is widely regarded as the most promising approach for mitigating sophisticated threats. This paper proposes an unsupervised anomaly detection method for multidimensional industrial network traffic. The feature engineering scheme for multidimensional industrial network traffic is specifically designed based on connection behavior characteristics, temporal features and statistical features. The deep autoencoder Gaussian mixture model (DAGMM) is employed and fine-tuned accordingly to generate normal behavior patterns with high-dimensional, large-scale traffic data considering the real-time response of the detection system. The proposed solution is extensively verified based on real network traffic data collected in the industrial control system (ICS) testbed. Numerical results confirm the effectiveness of the proposed model in modeling both statistical and mixed features of network traffic. The superiority in abnormal behavior identification and detection response is demonstrated compared to other models using a practical real-time framework.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"3 ","pages":"228-240"},"PeriodicalIF":0.0,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exponentially Convergent Algorithms Design for Distributed Resource Allocation Under Non-Strongly Convex Condition: From Continuous-Time to Event-Triggered Communication 非强凸条件下分布式资源分配的指数收敛算法设计：从连续时间到事件触发通信

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-12-30 DOI: 10.1109/TICPS.2024.3524368

Zhijun Guo;Junliang Xin;Qian Li

引用次数: 0