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针对无线传感器网络中的Sinkhole攻击问题,提出了一种基于蚁群优化(ACO)结合P2P信任模型的Sinkhole攻击检测(P-ACO)算法。首先,使用蚁群优化算法检测路由中是否存在Sinkhole攻击,并生成传感器节点的警报信息;然后,利用布尔表达式进化标记生成算法为群组警报节点分发密钥,并使用密钥标记可疑节点;最后,计算可疑节点列表中各节点的信任值,将信任值低于预设阈值的节点视为攻击节点。分析表明,相比二分查找算法与基于规则匹配的神经网络(RMNN)算法,该算法在匹配过程中需要更少的匹配搜索次数,提高了算法执行效率。实验结果显示,相比RMNN算法,该算法可以更加准确地检测Sinkhole攻击。
In order to solve Sinkhole attack in wireless sensor networks, a Sinkhole Attack Detection (P-ACO) algorithm based on ant colony optimization (ACO) and P2P trust model is proposed. Firstly, the ant colony optimization algorithm is used to detect the existence of Sinkhole attacks in the routing and generate the alarm information of the sensor nodes. Then, using the Boolean expression evolution token generation algorithm to distribute the keys for the group alarm nodes and use the key to mark the suspicious nodes. Finally, the trust value of each node in the list of suspicious nodes is calculated, and the node whose trust value is lower than the preset threshold is regarded as the attacking node. The analysis shows that compared with the binary search algorithm and the rule matching based neural network (RMNN) algorithm, the algorithm needs fewer matching search times in the matching process and improves the execution efficiency of the algorithm. Experimental results show that this algorithm can detect Sinkhole attacks more accurately than RMNN algorithm.