Effective combining of feature selection techniques for machine learning-enabled IoT intrusion detection

Abstract

The rapid advancement of technologies has enabled businesses to carryout their activities seamlessly and revolutionised communications across the globe. There is a significant growth in the amount and complexity of Internet of Things devices that are deployed in a wider range of environments. These devices mostly communicate through Wi-Fi networks and particularly in smart environments. Besides the benefits, these devices also introduce security challenges. In this paper, we investigate and leverage effective feature selection techniques to improve intrusion detection using machine learning methods. The proposed approach is based on a centralised intrusion detection system, which uses the deep feature abstraction, feature selection and classification to train the model for detecting the malicious and anomalous actions in the traffic. The deep feature abstraction uses deep learning techniques of artificial neural network in the form of unsupervised autoencoder to construct more features for the traffic. Based on the availability of cumulative features, the system then employs a variety of wrapper-based feature selection techniques ranging from SVM and decision tree to Naive Bayes for selecting high-ranked features, which are then combined and fed into an artificial neural network classifier for distinguishing attack and normal behaviors. The experimental results reveal the effectiveness of the proposed method on Aegean Wi-Fi Intrusion Dataset, which achieves high detection accuracy of up to 99.95%, relatively competitive to the existing machine learning works for the same dataset.