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.
Highlights
Internet of Things (IoT) is a self-organizing and adaptive network that interconnects uniquely identifiable "Things" to the internet via communication protocols [1]
This paper proposes a hybrid intrusion detection system for IoT, which relies on Principal Component Analysis (PCA) for dimension reduction, K-means for threats clustering, and Support Vector Machine (SVM) for anomaly classification
Apart from evaluating the classification accuracy, precision, DR, and FAR of the proposed intrusion detection model, we identified features selected by the PCA algorithm after feature dimension reduction
Summary
Internet of Things (IoT) is a self-organizing and adaptive network that interconnects uniquely identifiable "Things" to the internet via communication protocols [1]. The "Things" ( known as devices) are capable of sensing data from humans and the environment. IoT devices collect and sometimes store information that can be accessed pervasively and at any time. The Internet of Things (IoT) is a proliferating technology that offers many advantages in many areas of life [2]. The IoT is faced with several information security vulnerabilities and threats. Considering the intrinsic computational limitations of IoT devices and their vulnerabilities and the increasing rate of unauthorized access to these devices [3], IoT risks increase exponentially. Threats to the IoT network are similar to a traditional network, which threatens confidentiality, integrity, and availability. Such threats, when exploited, may lead to eavesdropping, data leakage/loss, and denial-of-service attacks [4]
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