Distributed filtering algorithm based on local outlier factor under data integrity attacks |
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| Institution: | 1. the Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai 200237, China;2. Shanghai Institute of Space Power-Sources, Shanghai 200245, China;3. State Key Laboratory of Space Power-sources Technology, Shanghai 200245, China;4. Shanghai Power & Energy Storage Battery System Engineering Tech Co. Ltd, Shanghai 200245, China;1. School of Automation, Chongqing University, Chongqing 400044, China;2. State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China;1. School of Cyber Security, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China;2. Department of Information Systems and Technology, Mid Sweden University, Sundsvall, Sweden;3. University of Sydney, Sydney, Australia;4. JD Explore Academy, Beijing, China;5. School of Information Science and Technology, Dalian Maritime University, Dalian, China;1. Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, PR China;2. Key Laboratory of System Control and Information Process, Ministry of Education, Shanghai 200240, PR China;3. Department of Mechnical Engineering, Politecnico di Milano, Milan 20156, Italy |
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| Abstract: | Network security is becoming a prominent issue for the development of information technology, and intelligent network attacks pose great challenges to system security due to its strong concealment. The existence of these attacks threatens the operation process of the complicated control system. Motivated by such a security problem, we study the secure distributed filtering algorithm under a kind of complex data integrity attack which can attack in two forms. We design a detection mechanism based on local outlier factor to distinguish the rightness of exchanged data, which determines whether to fuse the estimates by comparing the local density (LD) of the estimation of each sensor. Such a detection mechanism does not need the sensor to transmit redundant data information, thus greatly saving calculation cost and improving transmission efficiency. Meanwhile, we optimize the distributed filtering algorithm and obtain a suboptimal estimation gain. Finally, we demonstrate a numerical example to verify the availability of the filtering algorithm, and explore the influence of detector parameters on the performance of the estimation system. |
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