# Machine Learning Based Intrusion Detection Systems for IoT Applications

## TL;DR

This paper evaluates machine learning classifiers for detecting DoS attacks in IoT environments, using multiple datasets and statistical tests to identify the most effective approaches for intrusion detection.

## Contribution

It provides a comprehensive comparison of classifiers for IoT DoS detection, including statistical analysis and hardware response time evaluation, guiding future IDS development.

## Key findings

- Certain classifiers outperform others in detection accuracy
- Ensemble learning methods show promising results
- Response time varies significantly across classifiers on IoT hardware

## Abstract

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.