A Complex Network-Based Critical Node Identification Method for Industrial Control Systems Using Range of Failure Impacts

Critical node identification of Industrial Control System (ICS) can theoretically identify key components and help to allocate security protection resources reasonably. However, traditional critical node identification algorithms are difficult to adapt to the current security defense requirements. In this paper, we propose a critical node identification method adapted to the safety requirements of an ICS. In simulations, we use the Tennessee Eastman Process (TEP) as an example to test and analyze our critical node identification algorithm. The simulation results show that our algorithm can obtain criticality ranking results that meet the theoretical expectations. Moreover, the attack results on the TEP testbed show that the criticality ranking results obtained by our algorithm is 25% higher than that by the contrast algorithm.