Mitigation of malicious attacks on networks

By Christian M. Schneider, André A. Moreira, José S. Andrade Jr., Shlomo Havlin, Hans Jürgen Herrmann. Proceedings of the National Academy of Sciences of the United States of America, 2011

926 citations recorded by the source corpus.

Terrorist attacks on transportation networks have traumatized modern societies. With a single blast, it has become possible to paralyze airline traffic, electric power supply, ground transportation or Internet communication. How and at which cost can one restructure the network such that it will become more robust against a malicious attack? We introduce a new measure for robustness and use it to devise a method to mitigate economically and efficiently this risk. We demonstrate its efficiency on the European electricity system and on the Internet as well as on complex networks models. We show that with small changes in the network structure (low cost) the robustness of diverse networks can be improved dramatically whereas their functionality remains unchanged. Our results are useful not only for improving significantly with low cost the robustness of existing infrastructures but also for designing economically robust network systems.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/432c131f7017a2abe2743a08c4d95e50586e2a82

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Citations in the corpus

Robustness and resilience of complex networks (2024, background): “Regarding measuring damage, the most common approach 70 involves percolation-related metrics, such as monitoring the size of the largest connected component (LCC) and stopping the attack once it has reached a given target size, although other network metrics such as efficiency 59,71 or nestedness…”
Identifying a set of influential spreaders in complex networks (2016, methodology): “ng ability, i.e., ﬁxing the number of ﬁnal aﬀected nodes. We just take into account the ﬁrst form in our work, the other form can be analyzed similarly. Besides, some researchers use the robustness R [50], which is deﬁned as R = 1 n Pn i=1σ(i/n) where σ(i/n) is the fraction of nodes belonging to gia”
Networks formed from interdependent networks (2011, methodology): “The robustness of a network is usually either characterized by the value of the critical threshold analysed using percolation theory 52 or defined as the integrated size of the largest connected cluster during the entire attack process 53 .”
Attack Robustness and Centrality of Complex Networks (2013, background): “Interesting recent work has considered the evolution of network topologies that are robust to the removal of vertices based on their degree [12,13].”
Vital nodes identification in complex networks (2016, background): “ks exist. The pc value is determined by the highest one. Obviously, according to the objective function on network connectivity, the smaller the pc is, the better the ranking algorithm is. Robustness [320] is another measure to quantify the performance of ranking methods. It is deﬁned as the area un”
Catastrophic cascade of failures in interdependent networks (2009, background): “pology of their network like, Internet, airport connections, and power grids [7]. Recently, special emphasis has been focused on the robustness of such systems to random failures or malicious attacks [8, 9, 10, 11, 12, 13, 14]. Most of these works have been focused on single, isolated networks where”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “Nested networks were shown to characterize ecosystems and trade networks, while onion-networks are especially resistant against targeted attacks (Saavedra et al., 2011; Schneider et al., 2011; Wu & Holme, 2011).”