Method for Quantitative Estimation of the Risk Propagation Threshold in Electric Power CPS Based on Seepage Probability

Abstract

Because of the non-uniformity of the electric power CPS network and the dynamic nature of the risk propagation process, it is difficult to quantify the critical point of a cyber risk explosion. From the perspective of the dependency network, this paper proposes a method for quantitative evaluation of the risk propagation threshold of power CPS networks based on the percolation theory. First, the power CPS network is abstracted as a dual-layered network-directed unweighted graph according to topology correlation and coupling logic, and the asymmetrical balls-into-bins allocation method is used to establish a "one-to-many" and "partially coupled" non-uniform power CPS characterization model. Then, considering the directionality between the cyber layer and the physical layer link, the probability of percolation flow is introduced to establish the propagation dynamic equations for the internal coupling relationship of each layer. Finally, the risk propagation threshold is numerically quantified by defining the survival function of power CPS network nodes, and the validity of the proposed method is verified by the IEEE 30-bus system and 150-node Barabsi-Albert model. 2169-3536 (c) INDEX TERMS Electric power CPS, interdependent network, Percolation probability, Propagation dynamics

Cyber Risk Propagation and Optimal Selection of Cybersecurity Controls for Complex Cyberphysical Systems

“…The method was shown to be applicable for studying malware infection incidents, but it is questionable whether the epidemic outbreak model fits other types of incidents. Percolation theory was also used in Reference [ 24 ] to analyze the propagation of node failures in a network of CPSs comprising cyber and physical nodes organized in two distinct layers, such as in the case of the power grid. The Susceptible–Exposed–Infected–Recovered (SEIR) infectious disease model was used in Reference [ 25 ] to study malware infection propagation in the smart grid.…”

Risk-Aware Service Routes Planning for System Protection Communication Networks of Software-Defined Networking in Energy Internet

“…Aiming for the risk propagation in the smart grid, [27] leveraged the asymmetrical balls-into-bins allocation method and established the coupling relationship between the physical layer and the cyber layer in the CPS. The authors quantified the impact of risk propagation according to the percolation theory.…”

Risk Propagation Model and Its Simulation of Emergency Logistics Network Based on Material Reliability

“…Chen et al [19] established a credit risk transmission network model and discussed the influence of different factors on credit risk transmission through theoretical analysis and numerical simulation. In the field of power communication, Qu et al [20] presented a method to evaluate the risk propagation threshold of a power network, quantified the risk propagation threshold, and verified the effectiveness of the method using the model. Wang et al [21] paid attention to the fault risk in modern systems and studied its propagation mechanism with different coupled networks.…”