Secure distributed estimation under Byzantine attack and manipulation attack

Abstract

Wireless sensor networks (WSN) with distributed cooperation has been widely used in various fields due to their strong adaptive learning ability. However, WSN is vulnerable to malicious attacks, and the damaging behaviors of these attacks would make sensor nodes work unsatisfactorily and then contaminate the entire network. Although some security algorithms have been proposed to detect these malicious attacks, such as manipulation attack and Byzantine attack, they are not robust enough. To ameliorate this situation, a secure distributed diffusion least-mean-square (LMS) algorithm is designed, which adopts the dual detection mechanisms over the designed two subsystems. One subsystem is based on the LMS with cooperative strategy (L-CS), which uses an angle detector to filter manipulation attack, while the other subsystem is based on the LMS with non-cooperative strategy (L-NCS), where the sensors utilize non-cooperation to improve the detection effect on Byzantine attack. Moreover, the L-CS subsystem could further provide the secure estimation for the proposed algorithm by isolating malicious nodes. The performances are analyzed from the mean and mean-square convergence. Finally, some simulations are implemented to prove the effectiveness of the proposed algorithm.