Authors:
Darko Andročec
and
Neven Vrček
Affiliation:
Faculty of Organization and Informatics, University of Zagreb, Pavlinska 2, Varaždin and Croatia
Keyword(s):
Machine Learning, Internet of Things, IoT, Security, Systematic Review.
Related
Ontology
Subjects/Areas/Topics:
Data Communication Networking
;
Enterprise Information Systems
;
Internet of Things
;
Sensor Networks
;
Software Agents and Internet Computing
;
Software and Architectures
;
Telecommunications
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.