A Blockchain-Based Trust Evaluation Mechanism for Intrusion Detection in WSNs Using Improved Dove Swarm Optimization (IDSO) and Hybrid improved whale optimization algorithm with enhanced genetic Algorithm (IWOA-IGA)

Abstract

Block chain technology can provide an efficient security mechanism for creating a trusted network in Wireless Sensor Networks (WSNs). By utilizing distributed ledger technology, network nodes can securely exchange and verify information. This study further explores the potential of block chain technology in enhancing the security and trustworthiness of WSNs. An efficient security mechanism using block chain technology can be implemented in WSNs to establish a trusted network. Block chain ensures secure communication by providing decentralized consensus, data integrity, and tamper-proof verification. 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 Dove Swarm Optimization (IDSO). 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 significant features will be selected using hybrid improved whale optimization algorithm with enhanced genetic properties (IWOA-IGA). Finally blockchain-based trust evaluation mechanisms based intrusion detection will be performed based on Integrating Modified Elman Neural Network (MENN) with Graph Neural Networks (GNNs) called MENN-GNN 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 behaviour 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