Hit and Run: A Bayesian Game between Regular and Malicious Nodes in MANETs

In mobile ad hoc networks (MANETs), nodes can move freely. Besides conducting attacks to maximize their utility and cooperating with regular nodes to deceive them, malicious nodes get better payoffs with the ability to move. In this paper, we propose a game theoretic framework to analyze the strategy profiles for regular and malicious nodes. We model the situation as a dynamic Bayesian signaling game, and analyze and present the underlining connection between nodes' best combination of actions and the cost and gain of the individual strategy. Regular nodes consistently update their beliefs based on the opponents' behavior, while malicious nodes evaluate their risk of being caught to decide when to flee. Some possible countermeasures for regular nodes that can impact malicious nodes' decisions are pre- sented as well. An extensive analysis and simulation study shows that the proposed equilibrium strategy profile outperforms other pure or mixed strategies, and proves the importance of restricting malicious node's advantages brought by the flee option. I. INTRODUCTION Mobile ad hoc networks (MANETs) rely on collaboration between the participants to achieve the aimed functionalities. Their topologies dynamically change because of node move- ment. Nodes in MANETs usually have no pre-defined trust between each other. Moreover, all nodes tend to maximize their own utility (also referred to aspayoff ) in activities. Among existing research, different mechanisms (e.g. virtual currency, barter economy) have been developed to stimulate cooperation and mitigate nodes' selfish behavior. Besides regular nodes' selfish behavior, malicious nodes, which have different interests, also exist in the network. The common objective of malicious nodes is to maximize the damage to the network while avoiding being caught. Their utility comes from activities that disrupt the operation of the network and waste the resources of regular nodes. To counter malicious nodes and stimulate cooperation, reg- ular nodes monitor and continuously evaluate their neighbors. Certain criteria are set to distinguish a node's trust level towards others. Regular nodes will focus their resources on cooperating with neighbors they trust, decline requests from suspicious neighbors, and report when a neighbor is considered to be malicious. However, in this case, intelligent malicious nodes would elaborately choose a frequency at which they cooperate to deceive regular nodes. Moreover, in MANETs, malicious nodes have the strategy of fleeing to avoid punishment. Therefore, a malicious node can start its malicious behavior all over again with a clean history in a new location by fleeing before being caught. However, this additional strategy does not imply that malicious nodes should continuously hit and run, since fleeing is also associated with a cost (e.g. the energy spent to move to the selected destination). We can instinctively describe malicious nodes' optimal strategy as follows: cooperate to deceive reg- ular nodes' trust; attack to cause damage and maximize their own utility; flee before regular nodes accumulate enough evi- dence and decide to report. Now the critical questions become: