Network disruption and recovery: Co-evolution of defender and attacker in a dynamic game

The evolution of interactions between individuals or organizations are a central theme of complexity research. We aim at modeling a dynamic game on a network where an attacker and a defender compete in disrupting and reconnecting a network. The choices of how to attack and defend the network are governed by a Genetic Algorithm (GA) which is used to dynamically choose among a set of available strategies. Our analysis shows that the choice of strategy is particularly important if the resources available to the defender are slightly higher than the attackers'. The best strategies found through GAs by the attackers and defenders are based on betweenness centrality. Our results agree with previous literature assessing strategies for network attack and defense in a static context. However, our paper is one of the first ones to show how a GA approach can be applied in a dynamic game on a network. This research provides a starting-point to further explore strategies as we currently apply a limited set of strategies only.