Deep Learning Model of GRU Based Energy Effective Clustering and GAN Based Intrusion Detection in WSN

Wireless Sensor Networks (WSNs) are networks made up of many small devices that collect and send data. Clustering is a way to organize these devices into groups, where each group has a leader called a cluster head. These cluster heads (CHs) help manage communication within their groups. However, these networks can be vulnerable to attacks that disrupt their operations and compromise data. To address these security concerns, a new approach has been proposed based on two important components: Gated Recurrent Unit (GRU) clustering and CHs selection, and Generative adversarial networks (GAN) based intrusion detection. GRU clustering is a type of advanced algorithm that helps create clusters and choose the best CHs. It makes the network more efficient and secure. Additionally, GAN-based intrusion detection is a technique that can distinguish between normal and malicious behavior in the network. By training the system with different examples of attacks, it becomes good at identifying and classifying various types of intrusions. By combining GRU based clustering and GAN-based intrusion detection, the proposed approach aims to make WSN clustering more secure and efficient. This ensures that the network can effectively detect and handle attacks, protecting the integrity and reliability of the entire system.