Article
Automation & Control Systems
Yifang Zhang, Zheng-Guang Wu, Peng Shi
Summary: This article proposes two update strategies based on event/self-triggering for control protocols to handle the leader-following consensus in unreliable shared networks against denial-of-service (DoS) attacks. The strategies include a dynamic event-triggered communication scheme to mitigate unnecessary information transfer and a self-triggered communication function to save computation resources. The effectiveness of the proposed strategies and control protocols is verified through simulations.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Information Systems
Md Musabbir Hossain, Chen Peng
Summary: This study investigates observer-based predictive event-triggered load frequency control (LFC) for a multi-area power system in a smart grid with electric vehicles (EVs) under denial-of-service (DoS) attacks. By developing an event-triggering scheme based on the observer and presenting a model-based predictive control approach, stability under consideration of long-duration DoS attacks and external disturbances was derived.
INFORMATION SCIENCES
(2021)
Article
Automation & Control Systems
Xiaodan Zhang, Feng Xiao, Bo Wei, Mei Yu, Kaien Liu
Summary: This article investigates the resilient control for networked control systems in the presence of denial-of-service (DoS) attacks using a sampled-data and dynamic quantization scheme. A novel dynamic quantization strategy is designed for signal transmissions in the presence of DoS attacks. An estimator is introduced to design control laws and sufficient conditions are provided for the asymptotic stability of the system. An event-triggered communication scheme is also designed to reduce network resource consumption.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Zhiqiang Zuo, Xiong Cao, Yijing Wang, Wentao Zhang
Summary: This article investigates the consensus problem of multiagent systems subject to Denial-of-Service attacks from a control perspective. A distributed observer-based controller is proposed to reconstruct the agents' states, proving that consensus can still be achieved under DoS attacks. The article also addresses scenarios where DoS attacks simultaneously affect communication networks associated with the controller and the observer, providing a sufficient condition to maintain consensus performance of MASs. Numerical simulations are provided to illustrate the theoretical results.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Ning Zhao, Peng Shi, Wen Xing, Chee Peng Lim
Summary: This article addresses the event-triggered tracking control and filtering problem for Takagi-Sugeno fuzzy-approximation-based discrete-time nonlinear networked systems subject to the effect of denial-of-service attacks. Two novel resilient adaptive event-triggered mechanisms and an estimator are proposed to overcome the problem of unavailable system mode signal during the trigger interval and improve the stability of the system.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Energy & Fuels
Zhihong Huo, Bing Wang
Summary: In order to maintain the reliability and safety of wind-integrated power systems (WIPSs) and decrease the occupation of network channel, a novel distributed resilient multi-event cooperative triggered scheme is proposed. The resilient triggered scheme is based on H infinity load frequency sliding-mode control strategy which considers the random load disturbance and wind speed fluctuation in WIPSs. Compared with the traditional event triggered mechanism, the proposed mechanism provides stronger robustness and higher wind energy capture efficiency under denial of service attacks. Additionally, the resilient triggered scheme improves the utilization efficiency of network resources. The feasibility and effectiveness of the proposed strategy is validated through numerical analysis on a four-area WIPSs.
Article
Mathematics, Applied
Xingyue Liu, Kaibo Shi, Jun Cheng, Shiping Wen, Yajuan Liu
Summary: This paper develops an adaptive memory-based event triggering resilient LFC approach for multi-area power systems with wind power, considering denial-of-service (DoS) attacks in the communication network. The impact of each DoS attack is evaluated by the amount of continual information loss it causes, and an adaptive memory event triggering mechanism (AMETM) is proposed. A sampling-data LFC model is established based on the proposed AMETM and DoS attacks. The exponential stability criterion and the memory controllers design method are obtained to ensure the proposed memory control scheme can quickly stabilize the LFC system under a longer DoS attack.
APPLIED MATHEMATICS AND COMPUTATION
(2023)
Article
Engineering, Electrical & Electronic
Kang-Di Lu, Zheng-Guang Wu
Summary: This paper proposes a resilient event-triggered load frequency control (LFC) scheme with an additional control loop for cyber-physical power systems (CPPSs), specifically designed to handle denial-of-service (DoS) attacks. By introducing a resilient event-triggered communication scheme and establishing a novel switched LFC system model, this scheme ensures stability and control by considering both communication resources and cyber-security.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Automation & Control Systems
Zhen Han, Wei Wang, Jiangshuai Huang, Zhenqian Wang
Summary: This article addresses the issue of distributed adaptive formation tracking control of mobile robots in the presence of event-triggered communication (ETC) and denial-of-service (DoS) attacks. It considers the dynamic model of mobile robots and the directed-communication graph condition. Distributed event-triggered estimators are designed to handle the constraint of limited access to desired trajectory information. An adaptive tracking control scheme is then designed for each robot using the backstepping technique. The effects of DoS attacks on ETC are analyzed, and a stability condition is provided to ensure boundedness of all closed-loop signals and exclude Zeno behaviors. Experimental results validate the theoretical findings.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Engineering, Electrical & Electronic
Hui Zhao, Xuewu Dai, Lei Ding, Dongliang Cui, Jinliang Ding, Tianyou Chai
Summary: This paper investigates a cooperative control strategy for high-speed trains under DoS attacks through a resilient control scheme. A secure control framework is proposed to restore the communication network and ensure speed and position error constraints during train operation.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Computer Science, Information Systems
Jing Li, Wei Zhang, Zhaohui Zhang, Xiaobo Li, Xiaoli Yang
Summary: This paper proposes an event-triggering mechanism and a predictor compensation control strategy for cyber-physical systems under malicious denial-of-service attacks. The control strategy reduces data transmission and control updates by utilizing a novel event-triggering mechanism and a predictor to compensate for lost signals. The stability of the system is guaranteed by proving a sufficient condition.
INFORMATION SCIENCES
(2022)
Article
Engineering, Civil
Ning Zhao, Xudong Zhao, Meng Chen, Guangdeng Zong, Huiyan Zhang
Summary: This paper presents a resilient distributed event-triggered security control strategy to resist Denial of Service (DoS) attacks on connected vehicles. A switched sampled-data scheme is proposed to ensure that the control signal acts immediately when the attack ends. A co-design approach for controller gains and triggering parameters is given. Simulation and experimental studies demonstrate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Songlin Hu, Xiaohua Ge, Xiaoli Chen, Dong Yue
Summary: This paper addresses the issue of load frequency control (LFC) for islanded AC-MGs under simultaneous false data injection (FDI) attacks and denial-of-service (DoS) attacks. A new piecewise observer is constructed to estimate the system state and the FDI attack signal in real-time. A resilient H8 LFC scheme is developed to mitigate the impact of the attacks. The novelty lies in the use of an attack-parameter-dependent time-varying Lyapunov function approach for stability analysis and observer/controller design against concurrent FDI attacks and intermittent DoS attacks.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Engineering, Chemical
Wei Tan, He Wang, Huazhou Hou, Xiaoxu Liu, Meng Zheng
Summary: This paper deals with the resilient control problem for networked nonlinear systems (NNSs) with malicious attacks, and proposes a resilient dynamically triggering controller (RDTC) to mitigate the impact of attacks. The effectiveness of the proposed controllers and theory is demonstrated through experiments.
Article
Chemistry, Analytical
Xiao Zhang, Fan Yang, Xiang Sun
Summary: This paper investigates the problem of networked load frequency control of power systems against deception attacks. An adaptive event-triggered scheme is developed to adjust the number of triggering packets based on state changes in deception attacks, reducing the data-releasing rate while maintaining control performance. The proposed approach provides a trade-off between limited network communication resources and desired control performance.
Article
Computer Science, Hardware & Architecture
Jia-Nan Liu, Xizhao Luo, Jian Weng, Anjia Yang, Xu An Wang, Ming Li, Xiaodong Lin
Summary: An efficient, secure and privacy-preserving mobile cloud storage scheme is proposed, which protects data confidentiality and privacy, with fine-grained data structure, lightweight client-side computation and constant communication overhead, making it more suitable for MCS scenario.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Bi-Peng Chen, Yun Chen, Guo-Qiang Zeng, Qingshan She
Summary: This article focuses on the intelligent optimization issue using PEO-FOCNN, which combines fractional order convolutional neural networks (FOCNNs) with population extremal optimization (PEO). The Caputo fractional-order gradient method (CFOGM) is introduced to improve the optimization performance of FOCNN. The experiments demonstrate the superiority of PEO-FOCNN over other optimization algorithms on the MNIST dataset.
Article
Automation & Control Systems
Xiaohan Zhang, Xinghua Li, Yinbin Miao, Xizhao Luo, Yunwei Wang, Siqi Ma, Jian Weng
Summary: This article proposes a monitor-based usage control model that utilizes blockchain smart contract and software guard extensions to enable efficient data trading and full control of user identities and operations, effectively preventing data abuse and ensuring fair data exchange.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Automation & Control Systems
Kang-Di Lu, Zheng-Guang Wu
Summary: This article investigates the stealthy cyber-attack and its countermeasure in smart grids. A stealthy sparse cyber-attack model is proposed, and a constrained optimization problem is formulated to design the model by minimizing the number of contaminated meters. A generalized-cumulative-sum-based detector is developed to detect the proposed cyber-attacks. Numerical studies demonstrate the feasibility and effectiveness of the proposed countermeasure.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Information Systems
Haomiao Yang, Shaopeng Liang, Xizhao Luo, Dianhua Tang, Hongwei Li, Xuemin Shen
Summary: This article proposes a secure K-means clustering scheme called PIPC, which aims to protect the privacy and integrity of load profiling. By using techniques such as encrypted distance measurement and integrity assurance, PIPC successfully protects the privacy of smart-meter data and maintains the integrity of outsourced clustering.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Kang-Di Lu, Zheng-Guang Wu
Summary: This paper proposes a multi-objective stealthy false data injection attack scheme to improve the cyber-security of cyber-physical power systems. The attack model is described as a multi-objective optimization problem and a non-dominated sorting genetic algorithm II is introduced as the solver. A new representation mechanism is proposed to improve the efficiency of generating attack vector.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2022)
Article
Computer Science, Artificial Intelligence
Min-Rong Chen, Liu-Qing Yang, Guo-Qiang Zeng, Kang-Di Lu, Yi-Yuan Huang
Summary: This paper presents an improved firefly algorithm (IFA-EO) by incorporating extremal optimization (EO) and adaptive step size strategies to address the drawbacks of slow convergence rate and local optimum trapping in traditional firefly algorithm. Experimental results demonstrate that IFA-EO performs well on complex optimization problems.
Article
Automation & Control Systems
Kang-Di Lu, Zheng-Guang Wu, Tingwen Huang
Summary: This article proposes a novel three-stage dynamic false data injection attack (DFDIA) model in cyber-physical power systems (CPPS) by considering potential dynamic behaviors. It formulates the designing DFDIA as two constrained single-objective optimization problems and presents two versions of constrained differential evolution as solvers. It also proposes an interval state forecasting-based countermeasure to detect the established DFDIA and demonstrates their feasibility and effectiveness through extensive simulation experiments on IEEE bus systems.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Computer Science, Information Systems
Chongben Tao, Shiping Fu, Chen Wang, Xizhao Luo, Huayi Li, Zhen Gao, Zufeng Zhang, Sifa Zheng
Summary: The paper proposes a point-voxel-based 3-D dynamic object detection algorithm that improves the accuracy and generalization capabilities of object detection by aggregating local sensitive points and global features.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Automation & Control Systems
Jia-Cheng Huang, Guo-Qiang Zeng, Guang-Gang Geng, Jian Weng, Kang-Di Lu
Summary: This article presents a new automated deep learning method for optimizing the security of Industrial Internet of Things (IIoT). By using genetic algorithms (GA) to simultaneously optimize the hyperparameters and block-based architectures of convolutional neural networks (CNNs), it achieves better experimental results than existing manually designed models and neuron-evolutionary methods.
IET CYBER-SYSTEMS AND ROBOTICS
(2023)
Article
Computer Science, Information Systems
Jia-Cheng Huang, Guo-Qiang Zeng, Guang-Gang Geng, Jian Weng, Kang-Di Lu, Yu Zhang
Summary: This study proposes an automatic architecture design method of convolutional neural networks (CNNs) based on differential evolution (DE-CNN) for intrusion detection in industrial control systems (ICSs). The DE-CNN optimizes the architecture parameters of CNNs through an evolutionary process and selects the best individual based on validation accuracy and the number of CNN model parameters. Experimental results demonstrate the superiority of DE-CNN over manually-designed and neuroevolution-based methods in ICSs intrusion detection.
COMPUTERS & SECURITY
(2023)
Article
Computer Science, Artificial Intelligence
Kang-Di Lu, Le Zhou, Zheng-Guang Wu
Summary: This article proposes a representation-learning-based convolutional neural network (RL-CNN) for intelligent attack localization and system recovery of cyber-physical power systems (CPPSs). The RL-CNN is used as a multilabel classifier to improve the performance of attack localization by exploring and exploiting the implicit information of measurements. Additionally, a mean-squared estimator is employed to filter contaminated measurements and perform system recovery based on prior knowledge of the system state. Extensive simulation results demonstrate the effectiveness of the proposed method in achieving high accuracy for attack localization and automatic attack filtering.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Proceedings Paper
Automation & Control Systems
Kang-Di Lu, Zheng-Guang Wu
Summary: This paper proposes an ensemble learning-based cyber-attacks detection model for CPPS state estimation. The model uses subspace features and decision trees and random vector functional link networks as the basic classifier. Simulation results demonstrate the performance of the proposed model.
2022 INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS AND MECHATRONICS (ICARM 2022)
(2022)
Article
Engineering, Electrical & Electronic
Kang-Di Lu, Zheng-Guang Wu
Summary: This article proposes genetic-algorithm-based cumulative sum methods for online detection of jamming attacks in cyber-physical power systems (CPPSs). The proposed detectors are robust to time-varying jamming magnitudes and time-varying attacked locations, and show effectiveness in detecting jamming attacks in CPPSs.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
