Optimal Attack Strategy in Random Scale-Free Networks Based on Incomplete Information

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.