Cluster head based dual level node authentication model with node pattern analysis for intrusion detection in wireless sensor networks using machine learning

The application of Wireless Sensor Networks (WSNs) in military and civilian scenarios is on the rise but since they are operated in the open and unsecured world, they are extremely susceptible to routing attacks like wormhole, sinkhole, sybil, and black hole attacks. A WSN consists of a large number of small, low cost devices. They tend to get various types of assaults as they are deployed to an exposed and unsecured location. WSNs pose several security threats such as allowing attackers and other types of attacks to take advantage of the vulnerability of sensor nodes and the unreliability of wireless networks. This research suggests an Intrusion Detection System (IDS) that would be applicable to cluster based WSNs wherein within each cluster, a Cluster Head (CH) is chosen and collectively all the clusters choose a Group Head (GH) to oversee the behaviour of the node and the network. This research presents a Dual Level Node Authentication Model with Node Behaviour Analysis (DLNA-NBA) for Intrusion Detection. The proposed model considers machine learning model for accurate intrusion detection in the WSNs. The suggested outcome of this research demonstrates that the DLNA-NBA significantly enhances intrusion detection capability, achieving 98.9% accuracy in dual-level node authentication and 99.2% accuracy in intrusion detection, outperforming traditional models. The improved node verification, optimized feature utilization, and efficient behavioural pattern analysis collectively ensure secure communication and reduced false alarms in WSN environments.