The Evolutionary Game-Based Dynamics of Sybil Attack Prevention in WSN

Today, wireless sensor networks are widely employed in various applications, including monitoring environments and tracking objects for military surveillance, industrial applications, and healthcare. Thus, the establishment of security in such networks is of great importance. One of the dangerous attacks against these networks is the Sybil attack. In this attack, a malicious node propagates multiple fake identities simultaneously, which affects routing protocols and many other operations like voting, reputation evaluation, and data aggregation. In this paper, we study the sensor node rate trust decision to defend against Sybil attacks in WSNs and its dynamics that play a key role in stabilizing the whole WSN using evolutionary game theory. We then propose and prove the theorems indicating that evolutionarily stable strategies can be attained under different parameter values, which supply the theoretical foundations to devise defiance against Sybil attacks in WSNs. Moreover, we can find out the conditions that will lead SNs to choose the strategy Healthy as their final behavior. In this manner, we can assure WSNs’ security and stability by introducing a rate-trust mechanism to satisfy these conditions. And furthermore, the efficiency of algorithms in terms of true detection rate and false detection rate is evaluated through a series of experiments. Experiment results show that the proposed algorithm is able to detect 99.9% of Sybil nodes with a 0.005% false detection rate. Additionally, the proposed algorithm is compared with other algorithms in terms of true detection rate and false detection rate, which shows that the proposed algorithm performs satisfactorily.