A Multivariate Classification Algorithm for Malicious Node Detection in Large-Scale WSNs

WSN is a distributed network exposed to an open environment, which is vulnerable to malicious nodes. To find out malicious nodes among a WSN with mass sensor nodes, this paper presents a malicious detection method based on multi-variate 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 of 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%.