基于多元分类的无线传感器网络恶意节点检测算法

Multivariate Classification-Based Malicious Node Detection for Wireless Sensor Network

无线传感器网络是一个暴露在开放环境中的分布式网络,各节点之间相互独立,缺乏中心节点和监控节点,极易受到恶意节点的攻击。为了对无线传感器网络中的大量传感器节点进行恶意节点检测,提出了一种基于多元分类的恶意节点检测方法。提出的检测方法是在已知少量传感器节点类型的前提下,抽取与已知恶意节点类型相关的传感器节点属性,建立关于全部参与网络活动的传感器节点的样本空间,通过多元分类算法对已知类型传感器节点的样本进行学习,生成分类器,然后对未知类型传感器节点进行分类。实验结果表明在传感器节点属性值以及活跃节点个数稳定的情况下,误检率能够稳定在0.5%以下。

Wireless Sensor Network（WSN）is a distributed network exposed to an open environment.In a WSN,there are few central nodes or monitoring nodes and each node is independent.So WSNs are vulnerable to malicious nodes.In order to find out malicious nodes among a wireless sensor network with mass sensor nodes,this paper presents a malicious detection method based on multivariate classification.Given the types of a few sensor nodes,it extracts sensor nodes＇ preferences related with the known types of malicious node,establishes the sample space of all sensor nodes that participate in network activities.Then,according to the study on the type-known sensor nodes＇ samples based on the multivariate classification algorithm,a classifier is generated,and all the unknown-type sensor nodes are classified.The experiment results show that as long as the value of sensor nodes preferences and the number of active sensor nodes is stable,the false detection rate is stabilized under 0.5%.