Hybrid Feature Selection Models for Machine Learning Based Botnet Detection in IoT Networks

Abstract

Timely detection of intrusions is essential in IoT networks, considering the massive attacks launched by the huge-sized botnets which are composed of insecure devices. Machine learning methods have demonstrated promising results for the detection of such attacks. However, the effectiveness of such methods may greatly benefit from the reduction of feature set size as this may prevent the impeding impact of unnecessary features and minimize the computational resources required for intrusion detection in such networks having several limitations. This paper elaborates on feature selection methods applied to machine learning models which are induced for botnet detection in IoT networks. A particular attention is devoted to the use of wrapper methods and their combination with filter methods. While filter-based feature selection methods provide a computationally light approach to select the most informative features, it is shown that their utilization in combination with wrapper methods boosts up the detection accuracy.