An Efficient Clone Attack Detection Model using Artificial Immune Systems in Wireless Sensor Networks

Abstract

Security of sensor networks could be a very important challenge because the sensor nodes are deployed in unattended surroundings and that they are vulnerable to varied attacks. One of them is that the clone attack. In this, the truly insecure nodes area unit non-inheritable by the opponent to clone them by having a similar identity of the captured node, and also the opponent deploys an unplanned variety of clones throughout the sensor network. Therefore, cloned node detection is a significant challenge in sensor networks. Various clone node detection techniques are planned to detect these cloned nodes. These strategies incur management overheads and also the detection accuracy is low once the clone is chosen as a witness node. This paper proposes to resolve these problems by the Artificial Immune Systems based Clonal Selection Algorithm (CSA) (CSA) to identify the clones by choosing the suitable witness nodes. The benefits of the proposed methodology embrace (i) increase in the detection ratio, (ii) decrease in the control overhead, and (iii) increase in throughput. The performance of the proposed work is measured mistreatment detection ratio, packet delivery ratio, and average delay and control overheads. The implementation is finished mistreatment ns-2 to exhibit the reality of the proposed work.