Internet of Things (IoT) and its applications are the most popular research areas at present. The characteristics of IoT on one side make it easily applicable to real-life applications, whereas on the other side expose it to cyber threats. Denial of Service (DoS) is one of the most catastrophic attacks against IoT. In this paper, we investigate the prospects of using machine learning classification algorithms for securing IoT against DoS attacks. A comprehensive study is carried on the classifiers which can advance the development of anomaly-based intrusion detection systems (IDSs). Performance assessment of classifiers is done in terms of prominent metrics and validation methods. Popular datasets CIDDS-001, UNSW-NB15, and NSL-KDD are used for benchmarking classifiers. Friedman and Nemenyi tests are employed to analyze the significant differences among classifiers statistically. In addition, Raspberry Pi is used to evaluate the response time of classifiers on IoT specific hardware. We also discuss a methodology for selecting the best classifier as per application requirements. The main goals of this study are to motivate IoT security researchers for developing IDSs using ensemble learning, and suggesting appropriate methods for statistical assessment of classifier's performance.

翻译：物联网及其应用是当前最热门的研究领域。物联网的特性一方面使其易于应用于实际生活场景，但另一方面也使其面临网络威胁。拒绝服务攻击是针对物联网最具破坏性的攻击之一。本文研究了使用机器学习分类算法保护物联网免受DoS攻击的前景。我们对能够推动基于异常的入侵检测系统发展的分类器进行了全面研究。基于重要指标和验证方法对分类器性能进行了评估。使用CIDDS-001、UNSW-NB15和NSL-KDD等流行数据集对分类器进行基准测试。采用Friedman检验和Nemenyi检验从统计学角度分析分类器间的显著差异。此外，利用树莓派评估分类器在物联网专用硬件上的响应时间。我们还讨论了一种根据应用需求选择最佳分类器的方法。本研究的主要目标是激励物联网安全研究人员使用集成学习开发入侵检测系统，并建议采用适当方法对分类器性能进行统计评估。