An amalgamated correlation and regression based feature selection with ensemble learning approach for IoT network attack detection

Abstract

The advancements in the field of Information and Communication Technology (ICT) have led to the development of the Internet of Things (IoT), where ordinary things (e.g. smart meters, sensors etc.) can be connected and controlled over the internet. However, an IoT network is vulnerable to many malicious network attacks due to its inherent properties like limited computational abilities, heterogeneity, massive connectivity, etc. This paper presents a feature selection technique, which uses an amalgamation of LASSO regression and correlation based techniques, to find appropriate features for IoT network attack detection. The effectiveness of the proposed mechanism was evaluated on benchmark IoT network datasets using different machine learning techniques. The results revealed that the Gradient Boosting ensemble learning model achieved a maximum attack detection accuracy of 99.98%, and outperformed the other studied models.