Central nodes are critical in establishing structural connectivity in a complex network. Attacking such nodes can create real havoc in a complex system. We propose attack strategies based on four types of centers, namely betweenness center, degree center, median, and center. We study the vulnerability of synthetic as well as real-world networks in these center-based attacks. These attacks are node-removal attacks which involve identifying the central node set and removing them from the network. We observed that the attacks based on recalculated network information are more efficient than ones based on initial network information. This work shows that the median-based attack, which is a novel strategy proposed in this work, is highly destructive in real as well as synthetic networks.
Motter and Lai (2002) @cite_4 studied the security threat to complex networks by cascade of failures due to intentional attacks. Their findings underline that many real-world networks which are naturally heterogenous (and survive random attacks) are in fact highly prone to such cascading failures. Later Motter himself proposed a strategy for defending such attacks in heterogenous networks @cite_1 in 2004. Following these findings, in their work @cite_12 introduced a model which depicts that attack on a single node is sufficient to destroy the efficiency of the entire network.
@cite_1 @cite_4 @cite_12
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Complex networks with a heterogeneous distribution of loads may undergo a global cascade of overload failures when highly loaded nodes or edges are removed due to attacks or failures. Since a small attack or failure has the potential to trigger a global cascade, a fundamental question regards the possible strategies of defense to prevent the cascade from propagating through the entire network. Here we introduce and investigate a costless strategy of defense based on a selective further removal of nodes and edges, right after the initial attack or failure. This intentional removal of network elements is shown to drastically reduce the size of the cascade. Recent studies of attacks on complex networks suggest that small initial breakdowns can lead to global cascades of overload failures in communication, economic trading, and supply-transportation systems, considering the defense methods is very important to reduce the huge damage. The propagation of failures depends on the flow dynamics such as packet routing of physical quantities and on the heterogeneous distribution of load or capacity in many realistic networks which have topological properties of scale-free (SF) and degree-degree correlations. We introduce a defense strategy based on emergent rewirings between neighbors of the attacked node, and investigate the size of cascade on SF networks with controllable correlations. We show the differences of damaged size for the types of correlations and the effective range of tolerance in the defense methods. They can be performed on the current wireless communication or dynamic allocation technologies. We live in a modern world supported by large, complex networks. Examples range from financial markets to communication and transportation systems. In many realistic situations the flow of physical quantities in the network, as characterized by the loads on nodes, is important. We show that for such networks where loads can redistribute among the nodes, intentional attacks can lead to a cascade of overload failures, which can in turn cause the entire or a substantial part of the network to colla pse. This is relevant for real-world networks that possess a highly heterogeneous distribution of loads, such as the Internet and power grids. We demonstrate that the heterogeneity of these networks makes them particularly vulnerable to attacks in that a large-scale cascade may be triggered by disabling a single key node. This brings obvious concerns on the security of such systems. We study the vulnerability of complex networks under intentional attack with incomplete information, which means that one can only preferentially attack the most important nodes among a local region of a network. The known random failure and the intentional attack are two extreme cases of our study. Using the generating function method, we derive the exact value of the critical removal fraction fc of nodes for the disintegration of networks and the size of the giant component. To validate our model and method, we perform simulations of intentional attack with incomplete information in scale-free networks. We show that the attack information has an important effect on the vulnerability of scale-free networks. We also demonstrate that hiding a fraction of the nodes information is a cost-efficient strategy for enhancing the robustness of complex networks.