IEEE Transactions on Industrial Cyber-Physical Systems最新文献_第10页

Deep Learning Detection and Robust MPC Mitigation for EV-Based Load-Altering Attacks on Wind-Integrated Power Grids 深度学习检测和鲁棒 MPC 缓解基于电动汽车的风网负载调整攻击

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-07-09 DOI: 10.1109/TICPS.2024.3424769

Ahmadreza Abazari;Mohammad Mahdi Soleymani;Mohsen Ghafouri;Danial Jafarigiv;Ribal Atallah;Chadi Assi

{"title":"Deep Learning Detection and Robust MPC Mitigation for EV-Based Load-Altering Attacks on Wind-Integrated Power Grids","authors":"Ahmadreza Abazari;Mohammad Mahdi Soleymani;Mohsen Ghafouri;Danial Jafarigiv;Ribal Atallah;Chadi Assi","doi":"10.1109/TICPS.2024.3424769","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3424769","url":null,"abstract":"Large-scale deployment of electric vehicles (EVs) provides power grid operators with several opportunities, such as bidirectional energy transfers and frequency and voltage ancillary services. To fully realize these advantages, information and communication technologies (ICTs) between EV ecosystems and smart power grids have been developed, making power grids an appealing target for cyber attacks. On this basis, this paper studies the impact of a new family of EV-based load-altering attacks (EV-LAAs) against the subsynchronous control interaction (SSCI) of the wind-integrated power grid. First, the cyber-physical connections between the EV ecosystem and the power grid are discussed in detail to represent a threat model for coordinated EV-LAAs that can excite the SSCI modes of the system. Then, a convolutional neural network (CNN) is trained based on data from phasor measurement units (PMUs) at wind farm substations for detecting this attack, separating it from benign events, e.g., fault or line disconnection, and estimating attack vectors. The developed CNN detection model may neglect a few EV-LAAs due to the huge number of attack vectors with different combinations of amplitudes and frequencies during uncertainties in wind speeds and the number of WTG outages, leading to generating false negatives. As such, a robust model predictive controller (RMPC) is developed as a supplementary solution for mitigation purposes based on linear-matrix inequalities (LMIs). Possible uncertainties in wind speed and wind turbine generator (WTG) outages during different amplitudes of EV-LAAs are investigated when defining these LMIs. The performance of mitigation schemes is evaluated and compared with recent wide-area damping controllers, e.g., the two-degree freedom (2DOF), linear quadratic regulator (LQR), and \u0000<inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula>\u0000 under the co-simulation of EMTP-RV and MATLAB/Simulink.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"244-263"},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965674","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

Finite-Time Switching Resilient Control for Networked Teleoperation System With Time-Varying Delays and Random DoS Attacks 具有时变延迟和随机 DoS 攻击的网络远程操作系统的有限时间切换弹性控制

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-07-04 DOI: 10.1109/TICPS.2024.3422928

Lingyan Hu;Jiarun Huang;Shuang Hao;Shichao Liu;Jiecheng Lu;Bingyang Chen

{"title":"Finite-Time Switching Resilient Control for Networked Teleoperation System With Time-Varying Delays and Random DoS Attacks","authors":"Lingyan Hu;Jiarun Huang;Shuang Hao;Shichao Liu;Jiecheng Lu;Bingyang Chen","doi":"10.1109/TICPS.2024.3422928","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3422928","url":null,"abstract":"This paper presents a finite-time switching resilient controller for the networked teleoperation system control under time-varying delays and denial-of-service (DoS) attacks. The proposed controller comprises a proportional-differential plus damping (PD+d-like) controller and a switching resilient compensator. The first component, a PD+d-like controller, uses a continuous non-smooth function on the state errors and velocity signals to guarantee the global finite-time convergence. The latter part of the proposed controller, a switching resilient compensator, combines the zero-order holder (ZOH) with the continuous-time proportional-derivative (PD) regulator. This proposed controller could maintain global finite-time stability (GFTS) when time-varying delays and random DoS attacks simultaneously occur. Furthermore, we obtain the system stability criterion and establish relationships between controller parameters and maximum stability delay using Linear Matrix Inequality (LMI) technology for parameter tuning guidance. Both simulation and experimental results validate the resiliency of the proposed controller to time-varying delays and random DoS attacks.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"232-243"},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141729843","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

Deep Learning Aided Minimum Mean Square Error Estimation of Gaussian Source in Industrial Internet-of-Things Networks 工业物联网网络中高斯源的深度学习辅助最小均方误差估计

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-07-01 DOI: 10.1109/TICPS.2024.3420823

Majumder Haider;Md. Zoheb Hassan;Imtiaz Ahmed;Jeffrey H. Reed;Ahmed Rubaai;Danda B. Rawat

{"title":"Deep Learning Aided Minimum Mean Square Error Estimation of Gaussian Source in Industrial Internet-of-Things Networks","authors":"Majumder Haider;Md. Zoheb Hassan;Imtiaz Ahmed;Jeffrey H. Reed;Ahmed Rubaai;Danda B. Rawat","doi":"10.1109/TICPS.2024.3420823","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3420823","url":null,"abstract":"This article investigates the problem of estimating complex-valued Gaussian signals in an industrial Internet of Things (IIoT) environment, where the channel fading is temporally correlated and modeled by a finite state Markov process. To address the non-trivial problem of estimating channel fading states and signals simultaneously, we propose two deep learning (DL)-aided minimum mean square error (MMSE) estimation schemes. More specifically, our proposed framework consists of two steps, (i) a DL-aided channel fading state estimation and prediction step, followed by (ii) a linear MMSE estimation step to estimate the source signals for the learned channel fading states. Our proposed framework employs three DL models, namely the fully connected deep neural network (DNN), long short-term memory (LSTM) integrated DNN, and temporal convolution network (TCN). Extensive simulations show that these three DL models achieve similar accuracy in predicting the states of wireless fading channels. Our proposed data-driven approaches exhibit a reasonable performance gap in normalized mean square error (NMSE) compared to the genie-aided scheme, which considers perfect knowledge of instantaneous channel fading states.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"185-195"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602417","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 Group Coordination of Random Communication Constrained Cyber-Physical Systems Using Cloud Edge Computing 利用云边缘计算实现随机通信受限网络物理系统的分布式群组协调

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-06-27 DOI: 10.1109/TICPS.2024.3419756

Hongru Ren;Yinren Long;Hui Ma;Hongyi Li

引用次数: 0

Privacy-Prioritized Model Aggregation in ICPS: A Novel Approach to Federated Learning Aggregation With Lime and Blockchain ICPS 中的隐私优先模型聚合：利用莱姆和区块链实现联合学习聚合的新方法

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-06-27 DOI: 10.1109/TICPS.2024.3419751

Arshia Aflaki;Hadis Karimipour;Thippa Reddy Gadekallu

引用次数: 0

Intelligent Collision-Free Formation Control of Ball-Riding Robots Using Output Recurrent Broad Learning in Industrial Cyber-Physical Systems 在工业网络-物理系统中使用输出递归广义学习实现滑球机器人的智能无碰撞编队控制

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-06-20 DOI: 10.1109/TICPS.2024.3416410

Ching-Chih Tsai;Hsu-Chih Huang;Hsing-Yi Chen;Chi-Chih Hung;Shih-Ting Chen

{"title":"Intelligent Collision-Free Formation Control of Ball-Riding Robots Using Output Recurrent Broad Learning in Industrial Cyber-Physical Systems","authors":"Ching-Chih Tsai;Hsu-Chih Huang;Hsing-Yi Chen;Chi-Chih Hung;Shih-Ting Chen","doi":"10.1109/TICPS.2024.3416410","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3416410","url":null,"abstract":"This article presents an intelligent collision-free formation control method of ball-riding robots using an output recurrent broad learning strategy (ORBLS) in industrial cyber-physical systems (ICPS). A cyber ORBLS is incorporated with the backstepping sliding mode formation control (BSMFC) and potential field theory, called ICPS ORBLS-BSMFC, in order to attain collision-free formation control for the multiple ball-riding robots with uncertainties for ICPS, the proposed cyber ORBLS-BSMFC computing method is employed to address the robust self-balancing formation control problem of ICPS gyro-stabilized robots. A bi-directed and connected graph is used to mathematically model the inverse-atlas self-balancing robots with uncertainties in formation encountering unknown frictions, mass variations. Taking the feedback signals from the physical world, Lyapunov stability theory is utilized to prove that the cyber ORBLS-BSMFC control law makes the system asymptotically stable. Three simulations and two experimental results will manifest the effectiveness, superiority and merits of the proposed ICPS ORBLS-BSMFC with obstacle avoidance. Through comparative studies, the advantages of the proposed ICPS ORBLS-BSMFC computing are validated to accomplish collision-free formation control for ball-riding robots.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"459-470"},"PeriodicalIF":0.0,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142316406","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

An Intelligent Industrial Visual Monitoring and Maintenance Framework Empowered by Large-Scale Visual and Language Models 由大规模视觉和语言模型支持的智能工业视觉监控和维护框架

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-06-13 DOI: 10.1109/TICPS.2024.3414292

Huan Wang;Chenxi Li;Yan-Fu Li;Fugee Tsung

{"title":"An Intelligent Industrial Visual Monitoring and Maintenance Framework Empowered by Large-Scale Visual and Language Models","authors":"Huan Wang;Chenxi Li;Yan-Fu Li;Fugee Tsung","doi":"10.1109/TICPS.2024.3414292","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3414292","url":null,"abstract":"Industrial visual monitoring (IVM) is crucial for operation and maintenance, and artificial intelligence (AI) has excelled in this domain. As a revolutionary breakthrough in AI, large models are set to revolutionize IVM by advancing comprehensive automation and intelligence. This paper proposes an intelligent IVM and maintenance framework (IVMMF) empowered by large-scale visual and language models. Firstly, the proposed large-scale visual model comprehensively understands industrial images, providing accurate defect identification and descriptions. Subsequently, the local-knowledge-bases-based large language model was proposed to understand technical knowledge in specific fields, provide professional suggestions for engineers, and realize intelligent information interaction between the system and engineers. IVMMF achieves the intelligence of the entire process, including industrial image understanding, text dialogue, maintenance suggestions, and information communication. Finally, we construct a large-scale image-text IVM dataset, and the experiments demonstrate its exceptional performance, indicating its potential to transform the application paradigm in IVM.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"166-175"},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141453438","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

Intrusion Detection in Cyber-Physical Grid Using Incremental ML With Adaptive Moment Estimation 利用具有自适应矩估计的增量式 ML 在网络物理网格中进行入侵检测

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-06-12 DOI: 10.1109/TICPS.2024.3413607

Zhijie Nie;Sagnik Basumallik;P. Banerjee;Anurag K. Srivastava

{"title":"Intrusion Detection in Cyber-Physical Grid Using Incremental ML With Adaptive Moment Estimation","authors":"Zhijie Nie;Sagnik Basumallik;P. Banerjee;Anurag K. Srivastava","doi":"10.1109/TICPS.2024.3413607","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3413607","url":null,"abstract":"A novel online and adaptive machine-learning approach for network intrusion detection is proposed in this work with a use case of unknown attack detection in the industrial cyber-physical power grid. Existing machine-learning (ML) based-intrusion detection systems in cyber-physical power systems rely on a fixed dataset with known attack anomalies for training. These approaches can lead to \u0000<italic>poor detection accuracy</i>\u0000 as unknown cyber-attacks target the system. As a result, these ML approaches need to be \u0000<italic>re-trained from scratch</i>\u0000. This research proposes an adaptive network intrusion detection technique that identifies anomalies in industrial cyber-power grids and is capable of detecting unknown attacks with significant accuracy. The proposed intrusion detector, a neural network with adaptive moment estimation, incorporates an \u0000<italic>adaptive incremental learning</i>\u0000 when exposed to a new vulnerability. It can be deployed at the device level in the phasor measurement network systems and evolves with the latest knowledge-base of cyber threats. The proposed approach is validated using a real cyber-physical simulation environment consisting of real-time digital simulator, multiple hardware phasor measurement units, and a network simulator under two different scenarios of unknown attacks, and extensive analysis is performed for different network architecture, training epochs, choice of loss functions, and the volume of data utilized. Results show that the incremental approach improves the accuracy of brute-force attacks to \u0000<inline-formula><tex-math>$>99.9%$</tex-math></inline-formula>\u0000 and penetration-test attacks to 63.7%. Further, the applicability of our method is validated on two publicly available datasets where incremental learning improved DDoS attack detection accuracy to 97.7%, UDP attacks to 73.1%, DoS attacks to 99% and Scan attacks to 94.2%.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"206-219"},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602528","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

Early Detection of Cyber-Physical Attacks on Electric Vehicles Fast Charging Stations Using Wavelets and Deep Learning 利用小波和深度学习对电动汽车快速充电站的网络物理攻击进行早期检测

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-06-12 DOI: 10.1109/TICPS.2024.3413605

Ahmad M. Abu-Nassar;Walid G. Morsi

{"title":"Early Detection of Cyber-Physical Attacks on Electric Vehicles Fast Charging Stations Using Wavelets and Deep Learning","authors":"Ahmad M. Abu-Nassar;Walid G. Morsi","doi":"10.1109/TICPS.2024.3413605","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3413605","url":null,"abstract":"Transportation electrification plays an important role in the operation of the smart grid through the integration of the electric vehicle fast charging stations (EVFCSs), which allows the electric vehicles to provide regulation services to the grid through the vehicle-to-grid (V2G) concept. However, such an integration makes smart grid assets prone to cyber vulnerability threats. In this paper, a cyber-physical attack detection approach is developed to early detect such attacks. The proposed approach combines the continuous wavelet transform (CWT) and the convolution neural network (CNN) to provide an effective detection technique. The proposed detection approach has undergone rigorous testing that considered 420 realistic operational scenarios. Unlike in previous work, the proposed detection approach was found to be effective in automatically learning the salient features from the data as well as identifying the frequency bands that hold such features and using them in the classification process. Furthermore, this work investigated the cyber-attack detection accuracy using different time resolutions of smart meters. The results have shown that the proposed approach effectively detects cyber-physical attacks with an accuracy of 99.76% and a low computational time of 1.8 seconds.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"220-231"},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725561","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

Byzantine-Resilient Distributed Bandit Online Optimization in Dynamic Environments 动态环境中的拜占庭弹性分布式匪徒在线优化

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-06-06 DOI: 10.1109/TICPS.2024.3410846

Mengli Wei;Wenwu Yu;Hongzhe Liu;Duxin Chen

{"title":"Byzantine-Resilient Distributed Bandit Online Optimization in Dynamic Environments","authors":"Mengli Wei;Wenwu Yu;Hongzhe Liu;Duxin Chen","doi":"10.1109/TICPS.2024.3410846","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3410846","url":null,"abstract":"We consider the constrained multi-agent online optimization problem in dynamic environments that are vulnerable to Byzantine attacks, where some infiltrated agents may deviate from the prescribed update rule and send arbitrary messages. The objective functions are exposed in a bandit form, i.e., only the function value is revealed to each agent at the sampling instance, and held privately by each agent. The agents only exchange information with their neighbors to update decisions, and the collective goal is to minimize the sum of the unattacked agents' objective functions in dynamic environments, where the same function can only be sampled once. To handle this problem, a Byzantine-Resilient Distributed Bandit Online Convex Optimization (BR-DBOCO) algorithm that can tolerate up to \u0000<inline-formula><tex-math>$mathcal {B}$</tex-math></inline-formula>\u0000 Byzantine agents is developed. Specifically, the BR-DBOCO employs the one-point bandit feedback (OPBF) mechanism and state filter to cope with the objective function, which cannot be explicitly expressed in dynamic environments and the arbitrary deviation states caused by Byzantine attacks, respectively. We show that sublinear expected regret is achieved if the accumulative deviation of the comparator sequence also grows sublinearly with a proper exploration parameter. Finally, experimental results are presented to illustrate the effectiveness of the proposed algorithm.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"154-165"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326369","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