Detecting Selective Forwarding Attack in Cluster-Based Wireless Sensor Networks Using Composite Reputation Value Algorithm

Abstract

Wireless sensor networks (WSNs) consist of a generous number of small sensor nodes with low energy consumption, poor computing capabilities, and little storage. In large-scale data-gathering WSNs, cluster-based WSNs have been widely used. To identify a selective forwarding attack, the author used Composite Reputation Values (CRVs). The proposed technique has two phases of detection and correction working together to prevent attacks and increase the effective data transmission of the system. There are specific properties of WSN nodes, such as stable neighbors' information, that aid in detecting anomalies. When suspicious activity is detected, nodes report it to the Cluster Head (CH) for further investigation. Using the standard CRV Algorithm, the currently active nodes are grouped. This study also investigates whether or not it is possible to use the leaching method to examine the selective forwarding attack in the WSN. During the experiment, the highest forwarding rate of the CRV algorithm was observed in the range from 0.1000 to 0.9612, with 0.9612 being the highest forwarding rate.0.96