Toward Optimal DoS-Resistant Authentication in Crowdsensing Networks via Evolutionary Game

With the increasing demand of Quality of Service(QoS) in Crowdsensing Networks, providing broadcast authentication and preventing Denial of Service (DoS) attacks become not only a fundamental issue but also a challenging security service. The multi-level TESLA is a series of lightweight broadcast authentication protocols, which can effectively mitigate DoS attacks via randomly selected messages. However, the rule of the parameter selection still remains a problem. In this paper, we formulate the attack-defense model as an evolutionary game accordingly, and then present an optimal solution, which achieves security assurance along with minimum resource cost. We then analyze the stability of our evolutionary strategy theoretically. Simulation results are given to evaluate the performance of the proposed algorithm under low QoS channels and severe DoS attacks, which demonstrates that our proposed protocol canworks even in the extreme case.