Analysis of Rule-Based Classifiers for IDS in IoT

Abstract

The Internet of Things (IoT) networks suffered from different types of cyber attacks due to vulnerabilities present in IoT devices. The attacker creates Denial of Service (DoS) and Distributed DoS (DDoS) quickly towards IoT networks. Therefore, to secure IoT networks from such types of cyber attacks intelligent intrusion detection system is needed. This paper proposes the IDS with and without feature selection to detect DoS and DDoS attacks in IoT. The proposed system achieves higher accuracy of 99.9992% with a JRip classifier from the suite of rule-based classifiers using 36 features obtained using pre-processing data phase. The proposed approach brings relevant features using the correlation feature selection method with top-ranked 50% features and achieves higher accuracy of 99.9994% on IoT-BoT dataset compared to 36 features obtained after pre-processing data phase. The proposed system is compared with traditional IDSs in terms of the used feature selection method and dataset.