基于人工蜂群算法的双门限CRN网络攻击检测算法

为了提高认知无线电网络的攻击检测效果,提出一种基于人工蜂群算法的双门限认知无线电网络的攻击检测算法。收集网络中所有次级用户向融合中心发送的报告,对信号进行预处理并将信号传递至人工蜂群算法;蜂群的雇佣蜂阶段评估次级用户子集的适应度,观察蜂阶段根据解的相关信息开发可行解,选择最优的次级用户子集;观察蜂的结果传递回蚁群,并且更新蚁群的全局信息素值,蚁群对新解集进行开发操作。该算法结合人工蜂群算法与人工蚁群算法在全局搜索与局部开发之间达到平衡。仿真实验的结果显示,该算法实现了较高的检测率与较低的错误率,从而有效地优化了频谱利用率。

ATTACKS DETECTION ALGORITHM OF DOUBLE-THRESHOLD CRN NETWORKS BASED ON ARTIFICIAL BEE COLONY ALGORITHM

To improve the detection effect of cognitive radio network(CRN) attacks,this paper proposed an attacks detection algorithm of double-threshold CRN networks based on the artificial bee colony algorithm.Reports sent to the fusion center by all secondary users in the network were collected,and signals were pre-processed and transmitted to artificial bee colony algorithm;then,the fitness of secondary users subset of employed bees phase was evaluated,and possible solutions were developed by onlooker bees phase according to the related information of solutions,and the optimized secondary user subset were selected;results of onlooker bees phase are transmitted to the ant colony,and the global pheromone value was updated,and the new solution set were exploited by ant colony.The proposed algorithm combined the artificial bee colony with the ant colony optimization to balance the local exploitation and global exploration.Simulation experimental results show that the proposed algorithm achieves high detection probability and low error probability,and optimizes the spectrum utilization effectively.