Kalman and Cauchy clustering for anomaly detection based authentication of IoMTs using extreme learning machine

Abstract

AbstractThe vulnerabilities of the Internet of Things (IoTs) in general and the Internet of Mobile Things (IoMTs) in particular motivate researchers to equip them with security systems against intruders and attacks. The integration of anomaly detection with intrusion detection for IoMTs has not been addressed adequately. This paper tackles this issue through building a Kalman filter and Cauchy clustering algorithm for anomaly detection and using them for authentication nodes within IoMTs using the Extreme Learning Machine classifier. The algorithm of this proposed work is composed of various components; first, the Kalman filter‐based model for estimating the trajectory of pedestrians within an indoor environment based on fusing WiFi with IMU data. Second, trustworthiness assessment for detecting anomaly behaviour in IoMT based on the estimated trajectory using the Kalman filter. Third, the trust IDS model for IoMT systems by integrating anomaly detection with online learning for attacks identification using an online sequential extreme learning machine. The algorithm has been implemented and evaluated using TamperU dataset for WiFi fingerprinting and KDD99 for intrusion detection. Furthermore, a comparison with benchmarks (the algorithms which used in other studies) for intrusion and anomaly detection proves the superiority of this proposed approach in terms of all the considered classification metrics.