ID-WNFS: Intrusion Detection Using Whale Neuro-Fuzzy System In Wireless Sensor Network - Sharma, Rakesh; Athavale, Vijay Anant (International Journal of Computer Sciences and Engineering, 2025)
Intrusion detection in wireless sensor network (WSN) is a challenging research area, as the WSN has vast area, and lot of nodes. The wireless communication among the nodes, and the battery life of the nodes, makes the researchers difficult to establish a proper communication through the routing mechanism. This research develops the intrusion detection model by using the Neuro fuzzy model. The proposed Intrusion detection using Whale Neuro-Fuzzy System (WNFS) (ID-WNFS) is developed here for detecting the intruders present in the WSN environment. The proposed ID-WNFS has two components, sniffer for creating the log file, and detector for anomaly detection. The sniffer creates the log file by examining the transmission information and extracts the necessary features. The extracted features are sent to the detector, which has the WNFS for the anomaly detection. The proposed WNFS is created by including the properties of the whale optimization algorithm (WOA) with the Neuro fuzzy architecture. The optimization algorithm selects the appropriate fuzzy rules for the detection. The proposed ID-WNFS notifies the simulation protocol about the anomaly behaviour, and thus the routing path is built for the WSN. The entire simulation of ID-WNFS is done by introducing various attacks on nodes and the result reveal that, the ID-WNFS has achieved with the network lifetime as 43.989, energy as 7.106808 and the detection accuracy as 0.787191