Modelling a fuzzy based learning Approaches for intrusion detection In wireless sensor networks

Abstract

The emergence of wireless networking significantly relies on the self-organized and multi-hop network environment. It aggregates huge amount of sensor nodes through wireless communication and characterized as simpler and low cost network deployment. It is extensively adopted in real-time environment like military exploration, modern logistics, and environment perception where the connected sensor nodes collaboratively works to carry out detection, monitoring, and tracking of certain malicious nodes or intruders over the network. Specifically, WSN-based intrusion detection system is used to handle security issues encountered during rescuing of post-disaster, region monitoring, border patrol and turns as generic field of modern research. Thus, it needs constant monitoring and tracking method for the prediction of intrusion, thus there is a need for design to deal with these multi-objective constraints to attain high-quality and persistent handling of the intruder. This research concentrates on modelling three different phases which is discussed below: newline1) In the initial phase, Multi-Objective Particle Swarm Optimization (MOPSO) is used as classifier to enhance the identification of the rare attack. It provides better separability of various classes i.e. normal behaviour and false alarms. Here, Principal Component Analysis (PCA) is used as the method of feature selection to increase the performance of the classifier and Fuzzy Genetic Algorithm (FGA) is used to obtain better understanding of the proposed classifier newline