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Stealthy Attacks on Sampled-Data Control and Encryption-Based Countermeasures

IEEE Transactions on Automatic Control 2025年

作者： Shang, Jun Li, Yuzhe Chen, Tongwen Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

This paper investigates stealthy attacks on sampled-data control systems, where a continuous process is sampled periodically, and the resultant discrete output and control signals are transmitted through dual channels in wireless communication networks. Acknowledging the vulnerability of these transmitted data to cyber threats, we analyze two types of stealthy attacks that compromise either one or both channels. We explicitly present the necessary and sufficient conditions for the existence of stealthy single-channel attacks. To mitigate these security risks, we implement encryption-based countermeasures, including linear time-invariant and periodic encryption methods. We detail sufficient conditions under which these encryption strategies effectively thwart single- and dual-channel stealthy attacks. The effectiveness of these approaches and the theoretical findings are demonstrated through numerical simulations. © 1963-2012 IEEE.

关键词： Cyber attacks

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Multi-step Ahead Time Series Forecasting Based on the Improved Process Neural Networks 9th

Multi-step Ahead Time Series Forecasting Based on the Improv...

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9th International Conference on computer engineering and Networks, CENet2019

作者： Shao, Haijian Hu, Chunlong Deng, Xing Jiang, Dengbiao School of Computer Science and Engineering Jiangsu University of Science and Technology JiangsuZhenjiang China Key Laboratory of Measurement and Control for CSE School of Automation Ministry of Education Southeast University Nanjing China

ISBN: (纸本)9789811537523

This paper proposes a multi-step ahead time series forecasting based on the improved process neural network. The intelligent algorithm particle swarm optimization (PSO) is used to overcome the potential disadvantages of the neural network, such as slow convergence speed and derivative local minima. Firstly, the theoretical analysis of the PSO is given to optimize the multilayer perceptron (MLP) neural network architecture. Secondly, the theoretical analysis and processing flow about the MLP architecture optimization is given. Thirdly, the performance criteria are applied to verify the performance of the proposed approach. Finally, the experimental evaluation based on a typically chaotic time series with rich spectrum information is utilized to demonstrate that the proposed approach has comparative results and superior on forecasting accuracy comparing to the traditional methods. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Time series analysis

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Robust Event-Triggered Control of Nonlinear Systems Under a Global Sector-Bound Condition

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Guidance, Navigation and Control 2022年第2期2卷 59-79页

作者： Pengpeng Zhang Tengfei Liu Zhong-Ping Jiang Department of Control Science&Engineering Frontiers Science Center for Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819P.R.China Department of Electrical and Computer Engineering New York University370 Jay StreetBrooklynNY 11201USA

This paper studies an event-tniggered control problem for nonlinear systems subject to both external disturbancoes and dy namic *** is assumed that the system satisfies a global sector bound *** avold infnitely fast samplng,a novel eventriggred sampling mechanism is propoeed,which use8 not only the measuned system state but also an estimation of the inluence of the *** the propoeed design,the intersampling intervals an be lower bounded by a poeitive constant,and it is independent of botb external disturbances and dynamie ***,the doeedl loop event-tniggered system i proved to be input-torstate stable with repect to the extemal *** smalgain techmigues are;used for the stability analysis of the dloeeil-bop system.

关键词： Event-triggered control external disturbance dynamic uncertainty input-to-state stability

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DeceFL:a principled fully decentralized federated learning framework

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National science Open 2023年第1期2卷 35-51页

作者： Ye Yuan Jun Liu Dou Jin Zuogong Yue Tao Yang Ruijuan Chen Maolin Wang Chuan Sun Lei Xu Feng Hua Yuqi Guo Xiuchuan Tang Xin He Xinlei Yi Dong Li Guanghui Wen Wenwu Yu Hai-Tao Zhang Tianyou Chai Shaochun Sui Han Ding School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China Department of Applied Mathematics University of WaterlooWaterloo N2L 3G1Canada State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China School of Mathematical and Physical Sciences Wuhan Textile UniversityWuhan 430200China School of Electrical Engineering and Computer Science and Digital FuturesKTH Royal Institute of TechnologyStockholm 10044Sweden Shenyang Institute of Automation(SIA) Chinese Academy of SciencesShenyang 110169China School of Mathematics Frontiers Science Center for Mobile Information Communication and SecuritySoutheast UniversityNanjing 211189China Purple Mountain Laboratories Nanjing 211111China AVIC Chengdu Aircraft Industrial(Group)Co. Ltd.Chengdu 610091China

Traditional machine learning relies on a centralized data pipeline for model training in various applications;however,data are inherently *** a decentralized nature of databases presents the serious challenge for collaboration:sending all decentralized datasets to a central server raises serious privacy *** there has been a joint effort in tackling such a critical issue by proposing privacy-preserving machine learning frameworks,such as federated learning,most state-of-the-art frameworks are built still in a centralized way,in which a central client is needed for collecting and distributing model information(instead of data itself)from every other client,leading to high communication burden and high vulnerability when there exists a failure at or an attack on the central *** we propose a principled decentralized federated learning algorithm(DeceFL),which does not require a central client and relies only on local information transmission between clients and their neighbors,representing a fully decentralized learning *** has been further proven that every client reaches the global minimum with zero performance gap and achieves the same convergence rate O(1=T)(where T is the number of iterations in gradient descent)as centralized federated learning when the loss function is smooth and strongly ***,the proposed algorithm has been applied to a number of applications to illustrate its effectiveness for both convex and nonconvex loss functions,time-invariant and time-varying topologies,as well as IID and Non-IID of datasets,demonstrating its applicability to a wide range of real-world medical and industrial applications.

关键词： decentralized federated learning smart manufacturing control systems privacy

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Surrogate-Assisted Multi-Class Collaborative Teaching and Learning Optimizer for High-Dimensional Industrial Optimization Problems

Surrogate-Assisted Multi-Class Collaborative Teaching and Le...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jing Bi Ziqi Wang Haitao Yuan Jinhong Yang Jia Zhang College of Computer Beijing University of Technology Beijing China Beijing Laboratory of Smart Environmental Protection Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China CSSC Systems Engineering Research Institute Beijing China Dept. of Computer Science Lyle School of Engineering Southern Methodist University Dallas TX USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Swarm intelligence and evolutionary algorithms are widely applied in industrial scheduling, mobile edge computing, etc due to their strong robustness and fast optimization speed. However, some real-world industrial optimization problems involve numerous decision variables, known as high-dimensional problems. Current algorithms often require considerable computational resources to evaluate objective function values because of high-dimensional decision spaces. Moreover, they are also prone to be trapped in local optima. To solve the above problems, this work proposes an improved algorithm named Surrogate-assisted Multi-class Collaborative Teaching and learning optimizer (SMCT). A multi-class collaborative teaching and learning optimizer is proposed as a base optimizer to improve exploration and exploitation abilities. Furthermore, an autoencoder-assisted radial basis function is proposed as the surrogate model to replace true function evaluations, thereby saving computational resources and balancing the complexity and accuracy in fitting true models. Finally, experimental results demonstrate that SMCT surpasses its existing peers in both search accuracy and convergence speed across eight high-dimensional benchmark functions.

关键词： Accuracy Processor scheduling Computational modeling Education Autoencoders Collaboration Search problems Space exploration Particle swarm optimization Optimization

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Adaptive fuzzy predefined performance control for nonlinear switched interconnected systems with full-state constraints and actuator faults

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Information sciences 2025年 717卷

作者： Duan, Qi Liu, Zhi Lai, Guanyu Chen, C.L. Philip School of Automation Guangdong University of Technology Guangzhou China Pazhou Lab Guangzhou China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China

The study establishes an integrated control framework capable of maintaining stability and predefined output specifications for nonlinear interconnected switched systems while accounting for three critical constraints: mode-dependent average dwell time switching logic, actuator fault scenarios, and full-dimensional state limitations. Unlike existing adaptive predefined performance control methods, an improved state-scaling transformation mechanism is developed, which avoids strict constraints on coordinate transformations. By employing a novel stability analysis approach, the fuzzy adaptive framework is extended to mode-dependent average dwell-time nonlinear interconnected switched systems. The control architecture derived from Barrier Lyapunov Function theory ensures strict adherence to complete state constraints while regulating tracking errors within prescribed performance boundaries, ultimately achieving convergence to target precision within defined temporal parameters. Adaptive fault-tolerant compensation is realized via online parameter estimation mechanisms, effectively neutralizing actuator degradation effects while maintaining closed-loop stability. The efficacy of the proposed methodology is rigorously validated via two distinct experiments. © 2025

关键词： Lyapunov functions

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Stochastic process-based degradation modeling and RUL prediction: from Brownian motion to fractional Brownian motion

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science China(Information sciences) 2021年第7期64卷 5-24页

作者： Hanwen ZHANG Maoyin CHEN Jun SHANG Chunjie YANG Youxian SUN School of Automation and Electrical Engineering University of Science and Technology Beijing State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University Department of Automation Tsinghua University School of Automation and Electrical Engineering Linyi University Department of Electrical and Computer Engineering University of Alberta

Brownian motion(BM) has been widely used for degradation modeling and remaining useful life(RUL) prediction, but it is essentially Markovian. This implies that the future state in a BM-based degradation process relies only on its current state, independent of the past states. However, some practical industrial devices such as Li-ion batteries, ball bearings, turbofans, and blast furnace walls show degradations with long-range dependence(LRD), where the future degradation states depend on both the current and past degradation states. This type of degradation naturally brings two interesting problems, that is, how to model the degradations and how to predict their RULs. Recently, in contrast to the work that uses only BM, fractional Brownian motion(FBM) is introduced to model practical degradations. The most important feature of the FBM-based degradation models is the ability to characterize the non-Markovian degradations with LRD. Although FBM is an extension of BM, it is neither a Markovian process nor a semimartingale. Therefore, how to obtain the first passage time of an FBM-based degradation process has become a challenging task. In this paper, a review of the transition of RUL prediction from BM to FBM is provided. The peculiarities of FBM when addressing the LRD inherent in some practical degradations are discussed. We first review the BM-based degradation models of the past few decades and then give details regarding the evolution of FBM-based research. Interestingly, the existing BM-based models scarcely consider the effect of LRD on the prediction of RULs. Two practical cases illustrate that the newly developed FBMbased models are more generalized and suitable for predicting RULs than the BM-based models, especially for degradations with LRD. Along with the direction of FBM-based RUL prediction, we also introduce some important and interesting problems that require further study.

关键词： remaining useful life degradation model Brownian motion fractional Brownian motion long-range dependence

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Continuous Human Learning Optimization with Enhanced Exploitation 7th

Continuous Human Learning Optimization with Enhanced Exploit...

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7th International Conference on Life System Modeling and Simulation, LSMS 2021, and the 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021

作者： Wang, Ling Huang, Bowen Wu, Xian Yang, Ruixin Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronics Engineering and Automation Shanghai University Shanghai200444 China Department of Computer Science North Carolina State University RaleighNC United States

ISBN: (纸本)9789811672125

Human Learning Optimization (HLO) is an emerging meta-heuristic with promising potential. Although HLO can be directly applied to real-coded problems as a binary algorithm, the search efficiency may be significantly spoiled due to "the curse of dimensionality". To extend HLO, Continuous HLO (CHLO) is developed to solve real-values problems. However, the research on CHLO is still in its initial stages, and further efforts are needed to exploit the effectiveness of the CHLO. Therefore, this paper proposes a novel continuous human learning optimization with enhanced exploitation (CHLOEE), in which the social learning operator is redesigned to perform global search more efficiently so that the individual learning operator is relieved to focus on performing local search for enhancing the exploitation ability. Finally, the CHLOEE is evaluated on the benchmark problem and compared with CHLO as well as recent state-of-the-art meta-heuristics. The experimental results show that the proposed CHLOEE has better optimization performance. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Optimization

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EDSF: Fast and Accurate Ellipse Detection via Disjoint-Set Forest

EDSF: Fast and Accurate Ellipse Detection via Disjoint-Set F...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Jiang, Jingen Zhao, Mingyang Shen, Zeyu Yan, Dong-Ming Nlpr Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Academy of Artificial Intelligence Beijing China Institute of Software Chinese Academy of Sciences State Key Laboratory of Computer Science China Tsinghua University State Key Laboratory of Hydro-Science and Engineering Beijing China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

We present a novel yet effective method for detecting elliptical primitives in cluttered, occluded images, which has versatile applications in computer vision and multimedia processing fields. We begin by the fast extraction of smooth arcs from the edge map, followed by the construction of a directed graph and a disjoint-set forest, whereby the arc relationships are effectively encoded to enhance the arc grouping process. Compared with representative approaches such as the depth-first search, the disjoint-set forest enables complete grouping of arcs to generate candidate ellipses. Moreover, it merely has linear memory complexity and constant access time, hence guarantees fast detection. To boost precision and remove false positives, we propose to project the candidate ellipses onto the original image, to align the gradients of ellipses and the image pixels. We also vectorize the elliptical parameters to depress duplicated candidates. We perform extensive experiments on both synthetic and challenging real-world datasets, to show that our detector is accurate and efficient, as well as versatile in many practical tasks. The source code and datasets are available at https://***/xiaowuga/EDSF. © 2022 IEEE.

关键词： Directed graphs

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Passivity-Based Distributed Event-Triggered Flocking Control of Port-Hamiltonian Systems

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IEEE Control Systems Letters 2025年 9卷 342-347页

作者： Aranda-Escolástico, Ernesto Abdelrahim, Mahmoud Fernández-Amorós, David Guinaldo, María Colombo, Leonardo Department of Software and Systems Engineering Universidad Nacional de Educación a Distancia (UNED) Juan del Rosal 16 Madrid 28040 Spain Prince Sultan University Renewable Energy Laboratory College of Engineering Riyadh 11586 Saudi Arabia Computer Science and Automatic Control Department Universidad Nacional de Educación a Distancia (UNED) Juan del Rosal 16 Madrid 28040 Spain Centre for Automation and Robotics (CSIC-UPM) Ctra. M300 Campo Real Km 0200 Madrid Arganda del Rey 28500 Spain

This letter addresses the problem of flocking motion in the context of port-Hamiltonian systems. The port-Hamiltonian framework offers advantages in modeling networks, dissipation, and heterogeneous agents. In this regard, designing flocking controllers in port-Hamiltonian form significantly increases their practical applications. Additionally, event-triggered control has proven effective in reducing bandwidth consumption. However, challenges remain in combining flocking requirements with energy-dissipating terms. Therefore, we propose a novel event-triggered flocking controller suitable for port-Hamiltonian systems. The method is validated through numerical simulations of a group of hexarotors. © 2017 IEEE.

关键词： flocking passivity Port-Hamiltonian systems event-triggered control multi-agent systems

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