A Novel Intrusion Detection System for RPL-Based Cyber–Physical Systems

Abstract

The physical layer of cyber–physical systems (CPSs) is composed of resource-constrained devices connected in a wireless sensor network (WSN). Although this layer is easy to deploy, in most cases, it has many security issues. Several intrusion detection systems (IDSs) have been proposed and tested as effective and efficient solutions to detect only a few known attacks. In this article, we propose a novel, Supervised machine learning-based IDS that is capable of detecting several attacks. This article discusses all IDS design steps, starting from data collection to the feature engineering analysis and building the trained models. Experimental results show that the proposed IDS can detect four different types of attacks that were seen by the machine learning models during the training phase. The IDS can also detect the existence of several other attacks that are not seen by the model and classify them as unknown attack types. The proposed model achieves 99.97% classification accuracy when detecting known attacks and 85% classification accuracy when detecting a new attack type.