IEEE Transactions on Industrial Cyber-Physical Systems最新文献_第6页

Stealthy False Data Injection Attacks Against the Summation Detector in Cyber-Physical Systems 针对网络物理系统中求和检测器的隐形虚假数据注入攻击

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-08-20 DOI: 10.1109/TICPS.2024.3446469

Yifa Liu;Long Cheng;Dan Ye

引用次数: 0

Decentralized Switching-Type Adaptive Event-Triggered-Based Non-Recursive Control for Interconnected Cyber-Physical Systems Against Multiple Cyber Attacks 基于事件触发的分散交换式自适应非递归控制，用于互联网络物理系统对抗多重网络攻击

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-08-20 DOI: 10.1109/TICPS.2024.3446769

Haibin Sun;Yahui Cui;Linlin Hou;Ticao Jiao

引用次数: 0

Performance/Reliability Tradeoffs When Watermarking Cyber-Physical Systems 网络物理系统水印时的性能/可靠性权衡

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-08-19 DOI: 10.1109/TICPS.2024.3443772

C.M. Krishna

引用次数: 0

Anomaly Detection Using Bi-Directional Long Short-Term Memory Networks for Cyber-Physical Electric Vehicle Charging Stations 利用双向长短期记忆网络进行网络物理电动汽车充电站异常检测

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-08-02 DOI: 10.1109/TICPS.2024.3437349

Arif Hussain;Ankit Yadav;Gelli Ravikumar

{"title":"Anomaly Detection Using Bi-Directional Long Short-Term Memory Networks for Cyber-Physical Electric Vehicle Charging Stations","authors":"Arif Hussain;Ankit Yadav;Gelli Ravikumar","doi":"10.1109/TICPS.2024.3437349","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3437349","url":null,"abstract":"With the increasing integration of electric vehicles (EVs) into the distributed energy resources (DER) system, the security of EV charging stations (EVCS) from cyber-attacks is paramount. Utilizing deep learning and recurrent neural networks (RNNs) presents promising advantages in anomaly detection within power systems. Bi-directional long-short-term memory (Bi-LSTM) emerges as a viable choice for anomaly detection, offering distinct advantages that learn from both the forward and backward sequences of the data compared to conventional deep neural networks, RNNs, and basic LSTMs. This study proposes data-driven anomaly detection (DDAD) techniques using a Bi-LSTM network. Seven statistical features are extracted from the passive parameters (voltage, current, frequency, and SoC). Then, the wrapper feature selection method is used to identify the most relevant features, enhancing the accuracy of the proposed DDAD model. We generate a dataset of normal events such as line faults, load switching, capacitor switching, and cyberattack events, including denial-of-service (DoS), spoofing, replay, and data manipulation attacks, using an extended API integrated with RT-LAB to automate the process. We demonstrated the DDAD model on a DER-integrated EVCS microgrid model on a Hardware-in-Loop (HIL)-based intelligent Cyber Physical System (iCPS) testbed environment. Comprehensive experiments are conducted to evaluate the performance of our proposed DDAD model's accuracy, precision, recall, and F1 score with the testing dataset. We compared our results against LSTM, multi-layer perception (MLP), support vector machine (SVM), and linear regression (LR) techniques. This study emphasizes the development of an efficient approach for detecting anomalies on EVCS, and our results underscore the effectiveness of our proposed methodology, achieving an average testing accuracy of 99.42%, thereby reinforcing the cyber-physical security of EVCS.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"508-518"},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397337","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

GraphCCI: Critical Components Identification for Enhancing Security of Cyber-Physical Power Systems GraphCCI：增强网络物理电力系统安全的关键部件识别

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-08-02 DOI: 10.1109/TICPS.2024.3436647

Yigu Liu;Alexandru Ştefanov;Ioannis Semertzis;Peter Palensky

引用次数: 0

Deep Lagrangian Network Learning and Control of Robotic Exoskeleton Based on Multi-Sensor-Cyber Information Fusion 基于多传感器-网络信息融合的机器人外骨骼的深度拉格朗日网络学习与控制

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-08-02 DOI: 10.1109/TICPS.2024.3437347

Qing Guo;Haoran Zhan;Jiyu Zhang

引用次数: 0

Sliding-Mode Perturbation Observer-Based Delay-Independent Active Mitigation for AGC Systems Against False Data Injection and Random Time-Delay Attacks 基于滑动模式惰性扰动观测器的 AGC 系统延迟无关主动缓解技术，对抗虚假数据注入和随机时延攻击

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-07-31 DOI: 10.1109/TICPS.2024.3436188

Kaishun Xiahou;Xingye Xu;Deyang Huang;Wei Du;Mengshi Li

{"title":"Sliding-Mode Perturbation Observer-Based Delay-Independent Active Mitigation for AGC Systems Against False Data Injection and Random Time-Delay Attacks","authors":"Kaishun Xiahou;Xingye Xu;Deyang Huang;Wei Du;Mengshi Li","doi":"10.1109/TICPS.2024.3436188","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3436188","url":null,"abstract":"This article presents a sliding-mode perturbation observer (SMPO) based delay-independent active mitigation (DIAM) scheme for automatic generation control (AGC) systems of multi-area interconnected power grid. It is designed to defend against malicious cyber attacks such as false data injection attack (FDIA), random time-delay attack (RTDA), and coordinated cyber attack (CCA) in the measurement channel and control channel. In the DIAM scheme, perturbation terms are introduced to describe the comprehensive effects of injected false signals and random delay components caused by cyber attacks. SMPO is designed for each control area of AGC systems to reconstruct the perturbations in the measurement channel and control channel based on the equivalent output injection method. The cyber attacks are mitigated by compensating the perturbation terms based on the accurate perturbation estimations provided by SMPO. The proposed DIAM is a delay-independent scheme which does not require any time-delay knowledge, and it is able to deal with the coordinated attacks of FDIA and RTDA at the same time. Simulation studies and experimental tests are undertaken on a three-area AGC systems to demonstrate the performance of the proposed DIAM scheme.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"446-458"},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142230843","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

IP2FL: Interpretation-Based Privacy-Preserving Federated Learning for Industrial Cyber-Physical Systems IP2FL：基于解释的工业网络物理系统隐私保护联合学习

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-07-30 DOI: 10.1109/TICPS.2024.3435178

Danyal Namakshenas;Abbas Yazdinejad;Ali Dehghantanha;Reza M. Parizi;Gautam Srivastava

{"title":"IP2FL: Interpretation-Based Privacy-Preserving Federated Learning for Industrial Cyber-Physical Systems","authors":"Danyal Namakshenas;Abbas Yazdinejad;Ali Dehghantanha;Reza M. Parizi;Gautam Srivastava","doi":"10.1109/TICPS.2024.3435178","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3435178","url":null,"abstract":"The expansion of Industrial Cyber-Physical Systems (ICPS) has introduced new challenges in security and privacy, highlighting a research gap in effective anomaly detection while preserving data confidentiality. In the ICPS landscape, where vast amounts of sensitive industrial data are exchanged, ensuring privacy is not just a regulatory compliance issue but a critical shield against industrial espionage and cyber threats. Existing solutions often compromise data privacy for enhanced security, leaving a significant void in protecting sensitive information within ICPS networks. Addressing this, our research presents the \u0000<italic>IP2FL</i>\u0000 model, an Interpretation-based Privacy-Preserving Federated Learning approach tailored for ICPS. This model combines Additive Homomorphic Encryption (AHE) for privacy with advanced feature selection methods and Shapley Values (SV) for enhanced explainability. The proposed solution mitigates privacy concerns in federated learning, where traditional methods fall short due to computational constraints and lack of interpretability. By integrating AHE, the \u0000<italic>IP2FL</i>\u0000 model minimizes computational overhead and ensures data privacy. Our dual feature selection approach optimizes system performance while incorporating SV to provide critical insights into model decisions, advancing the field towards more transparent and understandable AI systems in ICPS. The validation of our model using ICPS-specific datasets demonstrates its effectiveness and potential for practical applications.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"321-330"},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965064","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

Secure Distributed Fusion Estimation Under Double Layer Defense Architecture 双层防御架构下的安全分布式融合估计

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-07-30 DOI: 10.1109/TICPS.2024.3435648

Pindi Weng;Tongxiang Li;Mingnan Hu;Jing Zhou;Bo Chen

引用次数: 0

Online Minority Cluster-Informed Semi-Supervised Random Vector Functional Link Network for Multi-Mode Intermittent Fault Diagnosis 用于多模式间歇性故障诊断的在线少数群组半监督随机向量功能链接网络

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-07-29 DOI: 10.1109/TICPS.2024.3434788

Wei Li;Pengyu Han;Zeyi Liu;Xiao He;Limin Wang;Tao Zhang

引用次数: 0