A game theoretic approach to cooperative intrusion detection

Abstract In Mobile Ad-Hoc Networks, cooperative intrusion detection is efficient and scalable to massively parallel attacks. However, due to concerns about privacy leakage and resource costs, if without enough incentives, most users are often selfish and disinterested in helping others to detect an intrusion event, thus an efficient incentive mechanism is required. In this paper, we formulate the incentive mechanism for cooperative intrusion detection as an evolutionary game and achieve an optimal solution to help nodes decide whether to participate in detection or not. Our proposed mechanism can deal with the problems that cooperative nodes do not own complete knowledge about other nodes. We develop a game algorithm to maximize nodes’ utility. To prevent malicious nodes from abusing our incentive mechanism, a punishment-appeal mechanism is proposed to penalize malicious behavior. Simulations demonstrate that our strategy can efficiently incentivize non-malicious nodes to cooperate and decrease the false detection rate.