A lightweight supervised intrusion detection mechanism for IoT networks

As the Internet of Things (IoT) is becoming increasingly popular, we have experienced more security breaches that are associated with the connection of vulnerable IoT devices. Therefore, it is crucial to employ intrusion detection techniques to mitigate attacks that exploit IoT security vulnerabilities. However, due to the limited capabilities of IoT devices and the specific protocols used, conventional intrusion detection mechanisms may not work well for IoT environments. In this paper, we propose a novel intrusion detection model that uses machine learning to effectively detect cyber-attacks and anomalies in resource-constraint IoT networks. Through a set of optimizations including removal of multicollinearity, sampling, and dimensionality reduction, our model can identify the most important features to detect intrusions using much fewer training data and less training time. Extensive experiments were performed on the CICIDS2017 and NSL-KDD datasets respectively to evaluate the proposed approach. The experimental results on two popular datasets show that our model has a high detection rate and a low false alarm rate. It outperforms existing models in multiple performance metrics and is consistent in classifying major cyber-attacks, respectively. Most importantly, unlike traditional resource-intensive intrusion detection systems, the proposed model is lightweight and can be deployed on IoT nodes with limited power and storage capabilities. (C) 2021 Published by Elsevier B.V.

Automated summary

The increasing popularity of the Internet of Things has led to more security breaches associated with vulnerable IoT devices, emphasizing the importance of employing intrusion detection techniques. Traditional intrusion detection mechanisms may not work well for IoT environments, leading to the proposal of a novel intrusion detection model utilizing machine learning. Through optimizations such as removal of multicollinearity and dimensionality reduction, the model shows promising results with high detection rates and low false alarm rates in experiments on popular datasets.