Enhanced Anomaly Detection System for IOT Based on Improved Dynamic SBPSO

Abstract

The Internet of Things (IoT) enables smart settings, which help human pursuits. Although the IoT has increased economic opportunities and made numerous human conveniences possible, it has also made it easier for intruders or attackers to take advantage of the technology by either attacking it or avoiding it. Therefore, the primary concerns for IoT networks are security and privacy. For Internet of Things (IoT) networks, several intrusion detection systems (IDS) have been developed thus far using various optimization techniques methods. But as data dimensionality has increased, the search space has grown significantly, offering difficult problems for optimization techniques like swarm optimization using particles (PSO). To overcome these obstacles, this work suggests an approach for feature selection dubbed enhanced to increase the sticky binary dynamic sticky binary particle swarm optimization's searchability, and particle swarm optimization (IDSBPSO) was developed. It introduced a method for decrease of the dynamic search space and various dynamic parameters (SBPSO). To identify malicious data flow in IoT networks, an IDS was developed using this methodology. The IoTID20 and UNSW-NB15 IoT network datasets were used to assess the proposed model. It was found that, even with less features, IDSBPSO typically attained accuracy that was either higher or comparable. Moreover, as compared to traditional IDSBPSO and PSO-based feature selection methods dramatically reduced computational cost and prediction time.