基于演化博弈的无线传感器网络入侵检测研究

针对无线传感器网络易遭受内部节点攻击的问题，本文提出了一种基于演化博弈的无线传感器网络入侵检测方法。将传感器网络的攻防对抗映射到博弈过程中，建立恶意节点和簇头节点之间的攻防博弈模型，改进传统复制动态方程，使得簇头节点在演化博弈过程中考虑到其他节点的历史策略，预测恶意节点的攻击策略；同时将改进复制动态方程应用于入侵检测算法，提高算法响应时间。实验表明，与传统方法的复制动态方程相比，采用该算法使得演化博弈能够快速达到均衡，收敛速度相比传统方法提高了80%,保障了网络安全性的同时避免了传感器网络检测能量的消耗。

Research on wireless sensor network intrusion detection based on evolutionary game

In the context of wireless sensor networks (WSNs), prone to internal node attacks, this study advances an intrusion detection approach underpinned by evolutionary game theory. The attack-defense confrontation of sensor networks is mapped into the game process, and the attack-defense game model between malicious nodes and cluster head nodes is established. The traditional replication dynamic equation is improved, so that the cluster head node takes the historical strategies of other nodes in the evolutionary game process to predict the attack strategy of malicious nodes. At the same time, the improved replication dynamic equation is applied to the intrusion detection algorithm to improve the response time of the algorithm. Experiments show that compared with the replication dynamic equation of the traditional method, the evolutionary game can quickly reach equilibrium by using this algorithm, and the convergence speed is 80% higher than that of the traditional method, which ensures the network security and avoids the consumption of sensor network detection energy.