Fault-tolerant AI-driven Intrusion Detection System for the Internet of Things

Internet of Things (IoT) has emerged as a key component of all advanced critical infrastructures. However, with the challenging nature of IoT, new security breaches have been introduced, especially against the Routing Protocol for Low-power and Lossy Networks (RPL). Artificial-Intelligence-based technologies can be used to provide insights to deal with IoT's security issues. In this paper, we describe the initial stages of developing, a new Intrusion Detection System using Machine Learning (ML) to detect routing attacks against RPL. We first simulate the routing attacks and capture the traffic for different topologies. We then process the traffic and generate large 2-class and multi-class datasets. We select a set of significant features for each attack, and we use this set to train different classifiers to make the IDS. The experiments with 5-fold cross-validation demonstrated that decision tree (DT), random forests (RF), and K-Nearest Neighbours (KNN) achieved good results of more than 99% value for accuracy, precision, recall, and F 1 score metrics, and RF has achieved the lowest fitting time. On the other hand, Deep Learning (DL) model, MLP, Naive Bayes (NB), and Logistic Regression (LR) have shown significantly lower performance. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper describes the development of an Intrusion Detection System using Machine Learning to detect routing attacks against RPL. Experimental results showed that decision tree, random forests, and K-Nearest Neighbours achieved good performance, while Deep Learning, Naive Bayes, and Logistic Regression performed poorly.