Optimized cyber-attack detection method of power systems using sliding mode observer

Abstract

This paper investigates the problem of automatic detection of cyber-attacks in cyber-physical systems (CPSs), where some of the state variables are corrupted by an attacker. An attack detector based on a sliding mode observer (SMO) is used to estimate the state attacks. The parameter values of the SMO-based detector have a significant role in attack detection time and also in attack detection accuracy. An on-time attack detection gives the operator or the automatic attack defender enough time to react efficiently against attacks. Moreover, attacks should be detected accurately in each state variable to be handled well. Hence, a new SMO-based attack detector with parameter adjustment is addressed in this paper using an optimization algorithm. The differential evolutionary algorithm is used to optimize detection time and detection accuracy in the presence of unknown attack vectors and adjust parameters such that attacks are detected correctly and as quickly as possible. The comparison of the simulation results on the IEEE 39-bus test system based on the proposed method and those of other available methods illustrate the capability of the optimized attack detection scheme in terms of detection accuracy and detection time in the presence of unknown attack vectors.