Sequential defense against random and intentional attacks in complex networks

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
Pin-Yu Chen, Shin-Ming Cheng

Abstract

Network robustness against attacks is one of the most fundamental researches in network science as it is closely associated with the reliability and functionality of various networking paradigms. However, despite the study on intrinsic topological vulnerabilities to node removals, little is known on the network robustness when network defense mechanisms are implemented, especially for networked engineering systems equipped with detection capabilities. In this paper, a sequential defense mechanism is first proposed in complex networks for attack inference and vulnerability assessment, where the data fusion center sequentially infers the presence of an attack based on the binary attack status reported from the nodes in the network. The network robustness is evaluated in terms of the ability to identify the attack prior to network disruption under two major attack schemes, i.e., random and intentional attacks. We provide a parametric plug-in model for performance evaluation on the proposed mechanism and validate its effectiveness and reliability via canonical complex network models and real-world large-scale network topology. The results show that the sequential defense mechanism greatly improves the network robustness and mitigat...Continue Reading

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Citations

Apr 15, 2016·Scientific Reports·Fuyu HuAn Zeng
Jan 20, 2021·PloS One·Alireza Ermagun, Nazanin Tajik

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