Wireless sensor networks (WSNs) has widely used in the practical-world applications, including the identification of the military targets, the monitoring of forest fires, the detection of medical and/or scientific targets, and, most importantly, in our everyday lives at home. However, because WSNs use broadcast transmission as their communication method and therefore lacks tamper resistance, adversaries can easily compromise WSNs. As a result, a hacker has the ability to listen in on all communication, replay past texts, insert suspensive data groups, and the compromised nodes. The two main security vulnerabilities that affect sensor nodes most frequently are the node and authentication of node compromise. This study proposes a heterogeneous structure for WSN intrusion detection and node capture. Using a cutting-edge method that combines a signature- based and anomaly-oriented methods through the neural network of multi-layer perceptron (MLP) classification through the clustering context, this framework effectively finds the recorded nodes. Additionally, the suggested architecture is effective at a very reasonable level of computation and cost of communication, it could provide a security barrier for actual application of WSN.

WSN, intrusion detection, multi-layer perceptron, wireless sensor networks, heterogeneous