Distributed Optimal and Self-Tuning Filters Based on Compressed Data for Networked Stochastic Uncertain Systems with Deception Attacks

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 1; p. 335
• Main Authors: Ma, Yimin, Sun, Shuli
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 28.12.2022; MDPI
• Subjects: Algorithms; Analysis; Data compression; Deception; distributed self-tuning filter; identification; Least squares method; Measuring instruments; multiplicative noise; Network security; Nodes; Noise; Safety and security measures; Self tuning; Sensors; unknown attack rate; weighted measurement fusion; unknown attack rate; distributed self-tuning filter; identification; weighted measurement fusion; multiplicative noise
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23010335

In this study, distributed security estimation problems for networked stochastic uncertain systems subject to stochastic deception attacks are investigated. In sensor networks, the measurement data of sensor nodes may be attacked maliciously in the process of data exchange between sensors. When the attack rates and noise variances for the stochastic deception attack signals are known, many measurement data received from neighbour nodes are compressed by a weighted measurement fusion algorithm based on the least-squares method at each sensor node. A distributed optimal filter in the linear minimum variance criterion is presented based on compressed measurement data. It has the same estimation accuracy as and lower computational cost than that based on uncompressed measurement data. When the attack rates and noise variances of the stochastic deception attack signals are unknown, a correlation function method is employed to identify them. Then, a distributed self-tuning filter is obtained by substituting the identified results into the distributed optimal filtering algorithm. The convergence of the presented algorithms is analyzed. A simulation example verifies the effectiveness of the proposed algorithms.