  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.
