Sequential Defense Against Random and Intentional Attacks in Complex Networks

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 firstly 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 mitigates the possibility of network disruption by acquiring limited attack status information from a small subset of nodes in the network.

13 pages, 14 figures

References in corpus (11)

• Emergence of scaling in random networks
• Statistical mechanics of complex networks
• The structure and function of complex networks
• Error and attack tolerance of complex networks
• Random graphs with arbitrary degree distributions and their applications
• Network robustness and fragility: Percolation on random graphs
• Resilience of the Internet to random breakdowns
• Breakdown of the Internet under intentional attack
• Scale-free topology of e-mail networks
• Robustness of the European power grids under intentional attack
• How to make a fragile network robust and vice versa

Cited by in corpus (6)

• Measuring the impact of spammers on e-mail and Twitter networks
• Measuring network resilience through connection patterns
• Robustness and stability of enterprise intranet social networks: The impact of moderators
• Optimal shattering of complex networks
• Assessing the risk of advanced persistent threats
• Ecology-Based DoS Attack in Cognitive Radio Networks