A Hybrid Intrusion Detection Model for Identification of Threats in Internet of Things Environment

Abstract

Internet of Things (IoT) has transcended from its application in traditional sensing networks such as wireless sensing and radio frequency identification to life-changing and critical applications. However, IoT networks are still vulnerable to threats, attacks, intrusions, and other malicious activities. Intrusion Detection Systems (IDS) that employ unsupervised learning techniques are used to secure sensitive data transmitted on IoT networks and preserve privacy. This paper proposes a hybrid model for intrusion detection that relies on a dimension reduction algorithm, an unsupervised learning algorithm, and a classifier. The proposed model employs Principal Component Analysis (PCA) to reduce the number of features in a dataset. The K-means algorithm generates clusters that serve as class labels for the Support Vector Machine (SVM) classifier. Experimental results using the NSL-KDD and the UNSW-NB15 datasets justify the effectiveness of our proposed model in detecting malicious activities in IoT networks. The proposed model, when trained, identifies benign and malicious behaviours using an unlabelled dataset.