Finite-time attack detection for nonlinear complex cyber-physical networks under false data injection attacks |
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| Institution: | 1. School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China;2. Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China;1. DIMES Department, Università della Calabria, Via Pietro Bucci, Cubo 42-C, Rende, CS 87036, Italy;2. DIMEG Department, Università della Calabria, Via Pietro Bucci, Cubo 42-C, Rende, CS 87036, Italy;1. School of Applied Mathematics, Nanjing University of Finance and Economics, Nanjing, Jiangsu, 210023, People’s Republic of China;2. Faculty of Information and Technology, Beijing University of Technology, Beijing, 100124, China;3. School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu, 610106, China;4. School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China;5. Key Laboratory of HPC-SIP (MOE), School of Mathematics and Statistics, Hunan Normal University, Changsha, 410081, Hunan, China;1. Department of Mathematics, Guizhou University, Guiyang, Guizhou 550025, PR China;2. Key Laboratory of High Performance Computing and Stochastic Information Processing (Ministry of Education of China), CHP-LCOCS, School of Mathematics and Statistics, Hunan Normal University, Changsha 410081, China;1. Department of Mathematics, Northeastern University, Shenyang, Liaoning 110004, China;2. School of Mechanical Engineering and Automation, Harbin Institute of Technology (Shenzhen), Shenzhen, China;3. Station for Drug and Instrument Control of Shenyang Joint Logistics Support Center, Shenyang, Liaoning, China |
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| Abstract: | This paper investigates the problem of finite-time attack detection for nonlinear complex cyber-physical networks under false data injection (FDI) attacks. Firstly, a Takagi-Sugeno (T-S) fuzzy model is used to approximate nonlinear complex cyber-physical networks in which the measurement channels are injected by FDI attacks. Secondly, based on adding a power integrator technique, a finite-time fuzzy observer is designed to achieve the rapid state observation of complex cyber-physical networks within a finite time by adjusting the observer parameters. Then, an attack detection mechanism consisting of the finite-time fuzzy observer and an attack detector is developed to detect FDI attacks, which can trigger an alarm within a finite time when FDI attacks occur. Finally, simulation results are given to show the effectiveness and superiority of the proposed method. |
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