Multi-objective PSO based feature selection for intrusion detection in IoT based wireless sensor networks

Abstract

Internet of Things (IoT) utilization is increasing every day in both industry and other applications recently. However, the IoT communication is under security threats from malicious users. In spite of the availability of multiple security solutions in the literature, the intruders are targeting the IoT sensor networks through multiple type of attacks. Therefore, an effective Intrusion Detection System (IDS) is required for safe-guarding the communication in the IoT environment. For this purpose, we propose an Intelligent Intrusion Detection System for detecting intruders in IoT based wireless sensor networks so that it is possible to handle such intrusions. For the development of this intelligent IDS, we propose a rule and Multi-Objective PSO based feature selection algorithm and also propose an intelligent rule based enhanced Multiclass Support Vector Machines classification algorithm for detecting the intruders more accurately. From the experiments conducted in this work by using KDD’99 Cup data set and CIDD data set for evaluation, it is proved that the proposed IDS is able to capture the intruders more accurately by enhancing the detection accuracy and also by reducing the false positive rate.