Machine Learning for the Internet of Things Security: A Systematic Review

Darko Andročec, Neven Vrček

2018

Abstract

Internet of things (IoT) is nowadays one of the fastest growing technologies for both private and business purposes. Due to a big number of IoT devices and their rapid introduction to the market, security of things and their services is often not at the expected level. Recently, machine learning algorithms, techniques, and methods are used in research papers to enhance IoT security. In this paper, we systematically review the state-of-the art to classify the research on machine learning for the IoT security. We analysed the primary studies, identify types of studies and publication fora. Next, we have extracted all machine learning algorithms and techniques described in primary studies, and identified the most used ones to tackle IoT security issues. We classify the research into three main categories (intrusion detection, authentication and other) and describe the primary studies in detail to analyse existing relevant works and propose topics for future research.

Paper Citation

in Harvard Style

Andročec D. and Vrček N. (2018). Machine Learning for the Internet of Things Security: A Systematic Review.In Proceedings of the 13th International Conference on Software Technologies - Volume 1: ICSOFT, ISBN 978-989-758-320-9, pages 563-570. DOI: 10.5220/0006841205630570

in Bibtex Style

@conference{icsoft18,
author={Darko Andročec and Neven Vrček},
title={Machine Learning for the Internet of Things Security: A Systematic Review},
booktitle={Proceedings of the 13th International Conference on Software Technologies - Volume 1: ICSOFT,},
year={2018},
pages={563-570},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006841205630570},
isbn={978-989-758-320-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Software Technologies - Volume 1: ICSOFT,
TI - Machine Learning for the Internet of Things Security: A Systematic Review
SN - 978-989-758-320-9
AU - Andročec D.
AU - Vrček N.
PY - 2018
SP - 563
EP - 570
DO - 10.5220/0006841205630570