Optimal Attack Strategy in Random Scale-Free Networks Based on Incomplete Information
LI Jun1, WU Jun1**, LI Yong2, DENG Hong-Zhong1, TAN Yue-Jin1**
1College of Information Systems and Management, National University of Defense Technology, Changsha 410073 2Department of Business Administration, Changsha University, Changsha 410073
Optimal Attack Strategy in Random Scale-Free Networks Based on Incomplete Information
LI Jun1, WU Jun1**, LI Yong2, DENG Hong-Zhong1, TAN Yue-Jin1**
1College of Information Systems and Management, National University of Defense Technology, Changsha 410073 2Department of Business Administration, Changsha University, Changsha 410073
摘要We introduce an attack model based on incomplete information, which means that we can obtain the information from partial nodes. We investigate the optimal attack strategy in random scale-free networks both analytically and numerically. We show that the attack strategy can affect the attack effect remarkably and the OAS can achieve better attack effect than other typical attack strategies. It is found that when the attack intensity is small, the attacker should attack more nodes in the "white area" in which we can obtain attack information; when the attack intensity is greater, the attacker should attack more nodes in the "black area" in which we can not obtain attack information. Moreover, we show that there is an inflection point in the curve of optimal attack proportion. For a given magnitude of attack information, the optimal attack proportion decreases with the attack intensity before the inflection point and then increases after the inflection point.
Abstract：We introduce an attack model based on incomplete information, which means that we can obtain the information from partial nodes. We investigate the optimal attack strategy in random scale-free networks both analytically and numerically. We show that the attack strategy can affect the attack effect remarkably and the OAS can achieve better attack effect than other typical attack strategies. It is found that when the attack intensity is small, the attacker should attack more nodes in the "white area" in which we can obtain attack information; when the attack intensity is greater, the attacker should attack more nodes in the "black area" in which we can not obtain attack information. Moreover, we show that there is an inflection point in the curve of optimal attack proportion. For a given magnitude of attack information, the optimal attack proportion decreases with the attack intensity before the inflection point and then increases after the inflection point.
LI Jun;WU Jun**;LI Yong;DENG Hong-Zhong;TAN Yue-Jin**
. Optimal Attack Strategy in Random Scale-Free Networks Based on Incomplete Information[J]. 中国物理快报, 2011, 28(6): 68902-068902.
LI Jun, WU Jun**, LI Yong, DENG Hong-Zhong, TAN Yue-Jin**
. Optimal Attack Strategy in Random Scale-Free Networks Based on Incomplete Information. Chin. Phys. Lett., 2011, 28(6): 68902-068902.
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2011, Vol. 28 
