Effective ensemble based intrusion detection and energy efficient load balancing using sunflower optimization in distributed wireless sensor network

Abstract

Wireless sensor networks (WSNs) play a very important role in providing real-time data access for big data and internet of things applications. Despite this, WSNs’ open deployment makes them highly susceptible to various malicious attacks, energy constraints, and decentralized governance. For mission-critical applications in WSNs, it is crucial to identify rogue sensor devices and remove the sensed data they contain. The resource-constrained nature of sensor devices prevents the direct application of standard cryptography and authentication techniques in WSNs. Low latency and energy-efficient methods are therefore needed. An efficient and safe routing system is created in this study. Initially the outliers are detected from deployed nodes using stacking based ensemble learning approach. Deep neural network (DNN) and long short term memory (LSTM) are two different basic classifiers and multilayer perceptron (MLP) is utilized as a Meta classifier in the ensemble method. The normal nodes are considered for further process. Then, distance, density and residual energy based cluster head selection and cluster formations are done. Sunflower optimization algorithm (SOA) is employed in this approach for routing purpose to improve energy efficiency and load balancing. Superior transmission routing can potentially obtained by taking the shortest way. This proposed method achieves 95% accuracy for the intrusion detection phase and 92% is the packet delivery ratio for energy efficient routing. Consequently, the proposed method is the most effective option for load balancing with intrusion detection.