分簇WSNs中基于安全数据融合的恶意行为检测方法

考虑到恶意节点篡改数据或者直接伪造数据对融合结果可能造成的影响，提出了一种基于安全数据融合的恶意行为检测机制来量化节点采集数据的可信程度，判断节点行为是否正常，并给出了恶意节点的发现与处理方法.仿真实验表明，该模型能够有效检测恶意节点，具有较高的检测率和较低的误检率.

A detection method for malicious nodes based on secure data aggregation in clustered wireless sensor networks

In wireless sensor networks， malicious nodes will tamper or falsify the data to affect the final fusion results. To solve this problem， an effective method for detecting malicious nodes based on secure data aggregation in clustered wireless sensor networks is proposed， which is able to quantify the uncertainty of node’s monitoring data and judge whether the node is credible or not. Besides， the specific malicious node’s detection and processing method is introduced. Simulation results show that the model is capable of detecting malicious nodes effectively， and achieving high detection rate and low false detection rate.