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Cluster-based Wireless Sensor Network (CWSN) is a kind of WSNs that becauseof avoiding long distance communications, preserve the energy of nodes and sois attractive for related applications. The criticality of most applications ofWSNs and also their unattended nature, makes sensor nodes often susceptible tomany 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 theircritical operations in CWSNs, their compromise and control by an attacker willdisrupt the entire cluster and sometimes the entire network, so their securityneeds more attentiveness and must be ensured. In this paper, we introduce ahybrid Intrusion Detection System (HIDS) for securing CHs, to take advantagesof both anomaly-based and misuse-based detection methods, that is highdetection and low false alarm rate. Also by using a novel preprocessing model,significantly reduces the computational and memory complexities of the proposedIDS, and finally allows the use of the clustering algorithms for it. Thesimulation results show that the proposed IDS in comparison to existing works,which often have high computational and memory complexities, can be as aneffective and lightweight IDS for securing CHs.

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