Machine Learning based Intrusion Detection for Cyber-Security in IoT Networks
Amine Khatib, Mohamed Hamlich, Denis Hamad
Abstract
IoT network is a promising technology, IoT implementation is growing rapidly but cybersecurity is still a loophole, detection of attacks in IOT infrastructures is a growing concern in the field of IoT. With the increased use of Internet of Things in different areas, cyber-attacks are also increasing proportionately and can cause failures in the system. IDS becomes the leading security solution. Anomaly based network intrusion detection (IDS) detection plays a major role in protecting networks against various malicious activities. Improving the security of loT networks has become one of the most critical issues. This is due to the large-scale development and deployment of loT devices and the insufficiency of Intrusion Detection Systems (IDS) to be deployed for the use of special purpose networks. In this article, the performance of several machine learning models has been compared to accurately predict attacks on IoT systems, the case of imbalanced classes was subsequently treated using the SMOTE technique. The Nystrom based kernel SVM is the first time used to detect attacks in the IoT network and the results are promising. The evaluation metrics used in the performance comparison are accuracy, precision, recall, f1 score, and auc-roc curve.