LAUSR

As a classic cyber-physical system, smart grids often suffer from various types of attacks, one of which the most threatening attacks is coordinated cyber-physical attack (CCPA). In order to improve the robustness of the smart grids against CCPA, we predict and simulate various possible attack scenarios, and propose three attack strategies, such as optimal attack strategy (OAS), saturation attack strategy (SAS) and decentralized random selective attack strategy (DRSAS). The OAS is used to get the maximum attack effect by attacking a small of number of targets in the case of limited resources. The SAS is designed to destroy the entire network with sufficient resource while avoiding repeated or redundant attacks. The DRSAS is proposed to random select decentralized cyber and physical targets according to attacker acknowledgement, real-time vulnerabilities, network characteristics, which is divided into implicit attack and explicit attack. In this paper, attack goals (AGs) function is firstly constructed to identify important cyber and physical targets. Secondly, based on these AGs, OAS and SAS are designed and applied to CCPA for analyzing the effect of those attack strategies on smart grids. Thirdly, The DRSAS is used to study the influence of decentralized random attack on cascading failure of smart grid, which applied to CCPA is to compare with OAS and SAS. Finally, node loss is used as an evaluation index to compare the attack effect of cyber attack (CA), physical attack (PA), CCPA based on OAS, SAS and DRSAS. The experimental results show that: (I) when the same proportion of nodes are removed, the CCPA based on the OAS or SAS has better attack effect rather than CA and PA; (II) CCPA based on DRSAS has different effects at different times, and explicit attack has better effect than implicit attack regardless of $$\alpha =0.3,0.4,0.5$$ and times t1, t2, t3.