Secure Distributed Fusion Estimation Under Double Layer Defense Architecture

This paper is concerned with the problem of secure fusion estimation for cyber-physical systems under false data injection (FDI) attacks and eavesdropping attacks. In this work, the active and passive defense mechanisms are respectively designed to preserve the privacy of local estimation information from eavesdroppers and to obtain satisfactory fusion estimation performance against FDI attacks. Specificly, encryption and decryption schemes are designed for the transmitted local estimates, which prevents the eavesdropper from obtaining the correct estimates while the monitoring center is not affected. Then, a secure fusion method is proposed consisting of encryption-based attack detection and prediction based compensation fusion, which can effectively reduce the impact of FDI attack signals. Finally, an illustrative example is employed to show the effectiveness and advantages of the proposed methods.