The security of wireless sensor networks is a hot topic in current research. Game theory can provide the optimal selection strategy for attackers and defenders in the attack-defense confrontation. Aiming at the problem of poor generality of previous game models, we propose a generalized Bayesian game model to analyze the intrusion detection of nodes in wireless sensor networks. Because it is difficult to solve the Nash equilibrium of the Bayesian game by the traditional method, a parallel particle swarm optimization is proposed to solve the Nash equilibrium of the Bayesian game and analyze the optimal action of the defender. The simulation results show the superiority of the parallel particle swarm optimization compared with other heuristic algorithms. This algorithm is proved to be effective in finding optimal defense strategy. The influence of the detection rate and false alarm rate of nodes on the profit of defender is analyzed by simulation experiments. Simulation experiments show that the profit of defender decreases as false alarm rate increases and decreases as detection rate decreases. Using heuristic algorithm to solve Nash equilibrium of Bayesian game provides a new method for the research of attack-defense confrontation. Predicting the actions of attacker and defender through the game model can provide ideas for the defender to take active defense.

无线传感器网络的安全是当前研究的热点。博弈论可以为攻防对抗中的攻击者和防御者提供最优的选择策略。针对以往博弈模型通用性较差的问题，提出一种广义贝叶斯博弈模型来分析无线传感器网络中节点的入侵检测。由于传统方法难以求解贝叶斯博弈的纳什均衡，提出并行粒子群优化来求解贝叶斯博弈的纳什均衡，并分析防守方的最优动作。仿真结果表明了并行粒子群优化算法相对于其他启发式算法的优越性。该算法被证明能够有效地寻找最优防御策略。通过仿真实验分析了节点的检测率和误报率对防御者收益的影响。仿真实验表明，防御者的收益随着误报率的增加而减少，随着检测率的降低而减少。利用启发式算法求解贝叶斯博弈的纳什均衡，为攻防对抗的研究提供了一种新的方法。通过博弈模型预测攻击者和防御者的行动，可以为防御者采取主动防御提供思路。