Securing Cluster-heads in Wireless Sensor Networks by a Hybrid Intrusion Detection System Based on Data Mining

Abstract

Cluster-based Wireless Sensor Network (CWSN) is a kind of WSNs that because of avoiding long distance communications, preserve the energy of nodes and so is attractive for related applications. The criticality of most applications of WSNs and also their unattended nature, makes sensor nodes often susceptible to many types of attacks. Based on this fact, it is clear that cluster heads (CHs) are the most attacked targets by attackers, And also according to their critical operations in CWSNs, their compromise and control by an attacker will disrupt the entire cluster and sometimes the entire network, so their security needs more attentiveness and must be ensured. In this paper, we introduce a hybrid Intrusion Detection System (HIDS) for securing CHs, to take advantages of both anomaly-based and misuse-based detection methods, that is high detection and low false alarm rate. Also by using a novel preprocessing model, significantly reduces the computational and memory complexities of the proposed IDS, and finally allows the use of the clustering algorithms for it. The simulation results show that the proposed IDS in comparison to existing works, which often have high computational and memory complexities, can be as an effective and lightweight IDS for securing CHs.