Trust-Based Intrusion Detection System (IDS) With Energy Efficient Secure Communication in Wireless Sensor Network Using Modified Chicken Swarm Optimization and Modified Elman Neural Network (MENN)

Abstract

Wireless Sensor Networks (WSNs) consists of tiny sensor nodes deployed in various geographic conditions to gather the information about the environment. The Intrusion Detection system (IDS) in Wireless Sensor Network is used to detect various attacks occurring on sensor nodes of WSNs that are placed in various hostile environments. In the last few years, many innovative and efficient approaches have emerged in this area, and we mainly focus on Trust based approaches of Intrusion Detection system. In this work initially all the nodes in the wireless sensor network will form clusters based on the communication range to reduce the energy consumption and to increase the network lifetime. And then cluster heads will be selected using Improved Chicken Swarm Optimization (ICSO). Once the nodes sensed the information it will send the gathered data to its cluster heads and cluster heads will transfer it to the base station. And then to detect attacks accurately significantfeatures will be selected using Improved Whale Optimization (IWOA). Finally trust based intrusion detection will be performed based on Modified Elman Neural Network (MENN) to establish secure communication in wireless sensor network. Also a Trust-Based Intrusion Detection System (IDS) in WSNs using the NSL-KDD'99 dataset combines trust management mechanisms and IDS techniques to detect and mitigate security threats in WSNs. This approach leverages the notion of trust to monitor and evaluate the behavior of sensor nodes and identify potential malicious activities. The system leverages the NSL-KDD'99 dataset for robust detection of security threats, extending network lifetime and reducing energy consumption.