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.

中文翻译：

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一种基于 RPL 的网络物理系统的新型入侵检测系统

信息物理系统 (CPS) 的物理层由连接在无线传感器网络 (WSN) 中的资源受限设备组成。尽管这一层易于部署，但在大多数情况下，它存在许多安全问题。已经提出并测试了几种入侵检测系统（IDS）作为仅检测少数已知攻击的有效和高效的解决方案。在本文中，我们提出了一种新颖的、基于监督机器学习的 IDS，它能够检测多种攻击。本文讨论了所有 IDS 设计步骤，从数据收集到特征工程分析和构建训练模型。实验结果表明，所提出的 IDS 可以检测机器学习模型在训练阶段看到的四种不同类型的攻击。IDS 还可以检测到模型未发现的其他几种攻击的存在，并将其归类为未知攻击类型。所提出的模型在检测已知攻击时达到 99.97% 的分类准确率，在检测新攻击类型时达到 85% 的分类准确率。