The Performance of IoT Malware Detection Technique Using Feature Selection and Feature Reduction in Fog Layer

Abstract

The rapid increase in the number of devices connected to internet (IoT) lead to expansion in the attacks that targeting these devices. One of these dangers attacks is malware which embedded with IoT devices that makes the detection for such malware is extremely challenging. The machine learning is one of the most effective techniques that used to detect different types of attacks in IoT environment. This technique includes three main stages: feature extraction, feature selection, and classification. The feature selection is the most important stage in ML technique because it contributes to minimizing the size of features which significantly accelerate the detection system. In this stage, most researchers trend to use one of three methods; feature selection, feature reduction and hybrid between feature selection and reduction. The present research aims to present a comparative study between the effect of using feature selection method and feature reduction method on the performance of the IoT malware detection system. The results showed that the proposed technique could achieved an accuracy about 97% when using feature selection method only. These results emphasize that feature selection method is more efficient than the feature reduction method in detection IoT malware.