Feature selection for intrusion detection system in Internet-of-Things (IoT)

Internet of Things (IoT) is suffered from different types of attacks due to vulnerability present in devices. Due to many IoT network traffic features, the machine learning models take time to detect attacks. This paper proposes a feature selection for intrusion detection systems (IDSs) using Information Gain (IG) and Gain Ratio (GR) with the ranked top 50% features for the detection of DoS and DDoS attacks. The proposed system obtains feature subsets using insertion and union operations on subsets obtained by the ranked top 50% IG and GR features. The proposed method is evaluated and validated on IoT-BoT and KDD Cup 1999 datasets, respectively, with a JRipclassifier. The system provides higher performance than the original feature set and traditional IDSs on IoT-BoT and KDD Cup 1999 datasets using 16 and 19 features, respectively. (C) 2021 The Korean Institute of Communications and Information Sciences (KICS). Publishing services by Elsevier B.V.

Automated summary

This paper proposes a feature selection method for intrusion detection systems using Information Gain and Gain Ratio, achieving higher performance in detecting DoS and DDoS attacks on IoT-BoT and KDD Cup 1999 datasets.