Electronic Science and Technology ›› 2021, Vol. 34 ›› Issue (12): 75-80.doi: 10.16180/j.cnki.issn1007-7820.2021.12.013

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Security State Estimation and Detection for Biasing Attack

SUN Yangyan,ZHOU Xiuying,REN Zhu   

  1. School of Information Science and Technology,Zhejiang Sci-Tech University,Hangzhou 310018,China
  • Received:2020-08-15 Online:2021-12-15 Published:2021-12-06
  • Supported by:
    National Natural Science Foundation of China(61403347);Natural Science Foundation of Zhejiang(LY17F030023)


Cyber-physical system is an intelligent system integrating computing, communication and control, which can realize the deep cooperation between network and physics. Biasing attack infuses constant false bias data by attacking operational data in information physical system, which leads to system state estimation error and affects the normal operation of the system. To solve this problem, the study adopts the attack detection model combining Kalman filter state estimator and t detector based on the least trace principle to detect the biasing attack of the measurement residual error of the system based on the optimal state estimation in the same attack scene. The detection of false biasing data is based on the measurement of the observed value of the residual, and the system is attacked according to the bias of the target observation function. Furthermore, the present study proposes the attack detection threshold of the target observation function and the t-test scheme based on hypothesis testing. MATLAB simulation shows that the scheme can detect the occurrence of biasing attack in a short time, and the detection rate is increased by more than 2% compared with traditional detection methods, and has stronger robustness.

Key words: cyber physical systems, false data injection, biasing attack, Kalman filtering, state estimation, t-test, hypothesis test, residual

CLC Number: 

  • TP393