A Novel Intrusion Detection System based on Machine Learning for Internet of Things (IoT) Devices

Abstract

An exponential increase has been observed in the amount of IOT devices. The demand for an intrusion detection system has increased with the proliferation of IOT devices. An intrusion detection system is made of machine learning algorithms or a combination of machine learning algorithms. These algorithms are used to identify and classify intrusions. This study compares the results obtained by applying Support Vector Classifier, Decision Tree Classifier and Random Forest Classifier on the CICIDS -17.