The Internet of Things (IoT) has become an enabler paradigm for different applications, such as healthcare, education, agriculture, smart homes, and recently, enterprise systems. Significant advances in IoT networks have been hindered by security vulnerabilities and threats, which, if not addressed, can negatively impact the deployment and operation of IoT-enabled systems. This article addresses IoT security and presents an intelligent two-layer intrusion detection system for IoT. The system's intelligence is driven by machine learning techniques for intrusion detection, with the two-layer architecture handling flow-based and packet-based features. By selecting significant features, the time overhead is minimized without affecting detection accuracy. The uniqueness and novelty of the proposed system emerge from combining machine learning and selection modules for flow-based and packet-based features. The proposed intrusion detection works at the network layer, and hence, it is device and application transparent. In our experiments, the proposed system had an accuracy of 99.15% for packet-based features with a testing time of 0.357 μs. The flow-based classifier had an accuracy of 99.66% with a testing time of 0.410 μs. A comparison demonstrated that the proposed system outperformed other methods described in the literature. Thus, it is an accurate and lightweight tool for detecting intrusions in IoT systems.

中文翻译：

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一种智能的两层物联网入侵检测系统

物联网 (IoT) 已成为不同应用的推动范式，例如医疗保健、教育、农业、智能家居以及最近的企业系统。物联网网络的重大进步受到安全漏洞和威胁的阻碍，如果不加以解决，可能会对物联网系统的部署和运营产生负面影响。本文介绍了物联网安全，并提出了一种用于物联网的智能两层入侵检测系统。该系统的智能由用于入侵检测的机器学习技术驱动，两层架构处理基于流和基于数据包的特征。通过选择重要的特征，在不影响检测精度的情况下，将时间开销降至最低。所提出的系统的独特性和新颖性来自结合机器学习和选择模块以实现基于流和基于数据包的特征。所提出的入侵检测工作在网络层，因此它是设备和应用程序透明的。在我们的实验中，所提出的系统对于基于数据包的特征的准确率为 99.15%，测试时间为 0.357 μs。基于流的分类器的准确率为 99.66%，测试时间为 0.410 μs。比较表明，所提出的系统优于文献中描述的其他方法。因此，它是一种用于检测物联网系统入侵的准确且轻量级的工具。在我们的实验中，所提出的系统对于基于数据包的特征的准确率为 99.15%，测试时间为 0.357 μs。基于流的分类器的准确率为 99.66%，测试时间为 0.410 μs。比较表明，所提出的系统优于文献中描述的其他方法。因此，它是一种用于检测物联网系统入侵的准确且轻量级的工具。在我们的实验中，所提出的系统对于基于数据包的特征的准确率为 99.15%，测试时间为 0.357 μs。基于流的分类器的准确率为 99.66%，测试时间为 0.410 μs。比较表明，所提出的系统优于文献中描述的其他方法。因此，它是一种用于检测物联网系统入侵的准确且轻量级的工具。