Efficiency of attack strategies on complex model and real-world networks

Abstract

We investigated the efficiency of attack strategies to network nodes when targeting several complex model and real-world networks. We tested 5 attack strategies, 3 of which were introduced in this work for the first time, to attack 3 model networks (Erdos and Renyi, Barabasi and Albert preferential attachment network, and scale-free network configuration models) and 3 real networks (Gnutella peer-to-peer network, email network of the University of Rovira i Virgili, and immunoglobulin interaction network). Nodes were removed sequentially according to the importance criterion defined by the attack strategy, and we used the size of the largest connected component (LCC) as a measure of network damage. We found that the efficiency of attack strategies (fraction of nodes to be deleted for a given reduction of LCC size) depends on the topology of the network, although attacks based on either the number of connections of a node or betweenness centrality were often the most efficient strategies. Sequential deletion of nodes in decreasing order of betweenness centrality was the most efficient attack strategy when targeting real-world networks. The relative efficiency of attack strategies often changed during the sequential removal of nodes, especially for networks with power-law degree distribution. © 2014 Elsevier B.V. All rights reserved

New attack strategies for complex networks

“…(ii) Attacks are more efficient with recalculated strategies than with initial strategies. The result matches the findings of other works [10,16,21]. (iii) The betweenness centrality based attack is more efficient than degree centrality based attack in degrading networks, which is consistent with previous works.…”

“…The attack strategies to complex networks were proposed based on network information such as degree centrality [9][10][11]13,16], betweenness centrality [12,17,16], eigenvector [18,16], closeness centrality [16,19], entropy [20], seconddegree neighbors [21], and so on. In them, Holme et al studied the behavior of complex networks subject to attacks on nodes and edges.…”

Combined Heuristic Attack Strategy on Complex Networks

“…Under such term, terrorist networks, fake-news spreading networks, malnets or botnets used in DDoS attacks, or for spreading worms and viruses, dark networks involved in various criminal activities like illegal arm selling or child pornography, and so forth can be understood [14][15][16][17][18][19][20]. Attack strategies on such harmful complex networks (i.e., node deletion and occasionally also edge deletion) are studied in [21][22][23]. For example, in terrorist networks, a sequence of individuals should be identified, whose arrest will result in the maximum breakdown of communication between remaining individuals in the network.…”

Vulnerability of Complex Networks under Approximate Longest Chain Attack Strategies

“…In recent decades, many researchers proposed many attack strategies and analyzed vulnerability of various complex network models and real world networks [1][2][3][4], ranging from biology to Internet [5,6], power to transportation [7][8][9].…”