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 F1-score metrics, and RF has achieved the lowest fitting time. On the other hand, Deep Learning (DL) model, MLP, Naïve Bayes (NB), and Logistic Regression (LR) have shown significantly lower performance.

物联网（IoT）已成为所有高级关键基础架构的关键组件。但是，由于物联网具有挑战性，因此引入了新的安全漏洞，尤其是针对低功耗和有损网络（RPL）的路由协议。基于人工智能的技术可用于提供见解，以解决物联网的安全问题。在本文中，我们描述了开发的初始阶段，即使用机器学习（ML）来检测针对RPL的路由攻击的新入侵检测系统。我们首先模拟路由攻击，并捕获不同拓扑的流量。然后，我们处理流量并生成大型2类和多类数据集。我们为每种攻击选择一组重要功能，然后使用此组来训练不同的分类器以制作IDS。F 1分指标，并且RF达到了最短的拟合时间。另一方面，深度学习（DL）模型，MLP，朴素贝叶斯（NB）和逻辑回归（LR）已显示出明显较低的性能。