Model and Analyze the Cascading Failure of Scale-Free Network Considering the Selective Forwarding Attack

Abstract

Many real-world networks have scale-free characteristics and can be abstracted into scalefree networks. Aiming at the problem that scale-free networks have low fault tolerance in the face of malicious attacks, we focus on the selective forwarding attack behavior that exists widely in the networks, and build a selective forwarding attack model based on node importance. Moreover, according to the neighbor node's malicious and non-malicious behavior, a load redistribution strategy for failed node is proposed. Then, the network's damage degree is given to evaluate the comprehensive impact of cascading failure phenomenon on network connectivity and information integrity under selective forwarding attack. Finally, a cascading failure model of scale-free networks considering selective forwarding attack behavior is obtained. Based on this model, the propagation condition without triggering network cascading failure, the selective forwarding attack intensity threshold and the load loss ratio are obtained. By simulation on the classical BA scale-free network model, the results show that multiple nodes' random failure occurs in scalefree networks, selective forwarding attack behavior is helpful to improve the connectivity of the network. Besides, the network exists selective forwarding attack intensity threshold, when attack intensity is greater than the intensity threshold, the malicious nodes will not fail because of the failure of the neighbor nodes. But, selective forwarding attack behavior can destroy the information integrity, and there is a negative correlation between the attack intensity and the information integrity. These results have certain guiding significance for cascade failure analysis and prevention method research and design in real life. INDEX TERMS Scale-free network, cascading failure, selective forwarding attack, attack intensity threshold, information integrity This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

A Memetic Algorithm for Solving the Robust Influence Maximization Problem on Complex Networks against Structural Failures

“…In malicious attacks, targets are attacked in the order of their importance; for example, nodes with larger degrees tend to be removed in priority [ 14 ]. Generally, malicious attacks are likely to cause more distinct structural losses than random attacks; therefore, such attack model has been intensively studied in previous studies including the attack on connectivity [ 15 , 16 , 17 ], on community [ 18 , 19 ], and on the diffusion behavior [ 13 ]. Several reasonable robustness performance evaluation factors have been designed [ 20 , 21 ]; based on these, methods that can improve the robustness are also developed [ 13 , 20 , 22 , 23 , 24 ].…”