sesa 18: e3

Research Article

A Comprehensive Survey on Intrusion Detection based Machine Learning for IoT Networks

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  • @ARTICLE{10.4108/eai.6-10-2021.171246,
        author={Hela Mliki and Abir Hadj Kaceam and Lamia Chaari},
        title={A Comprehensive Survey on Intrusion Detection based Machine Learning for IoT Networks},
        journal={EAI Endorsed Transactions on Security and Safety: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SESA},
        year={2021},
        month={10},
        keywords={Internet of Things (IoT), Wireless sensor Network (WSN), Machine Learning (ML), Intrusion Detection (ID)},
        doi={10.4108/eai.6-10-2021.171246}
    }
    
  • Hela Mliki
    Abir Hadj Kaceam
    Lamia Chaari
    Year: 2021
    A Comprehensive Survey on Intrusion Detection based Machine Learning for IoT Networks
    SESA
    EAI
    DOI: 10.4108/eai.6-10-2021.171246
Hela Mliki1,2,*, Abir Hadj Kaceam2, Lamia Chaari2
  • 1: Science Faculty of Gabes, University of Gabes, Tunisia
  • 2: Laboratory of Technology and Smart Systems (LT2S), Digital Research Center of Sfax (CRNS), University of Sfax, Tunisia
*Contact email: mliki.hela@gmail.com

Abstract

The Internet of things (IoT) is a new ubiquitous technology that relies on heterogeneous devices and protocols. The IoT technologies are expected to offer a new level of connectivity thanks to its smart devices able to enhance everyday tasks and facilitate smart decisions based on sensed data. The IoT could collect sensitive data and should be able to face attacks and privacy issues. The IoT security issue is a hot topic of research and industrial concern. Indeed, threats against IoT devices and services could cause security breaches and data leakage. Aiming to identify attempts to abuse the IoT systems and mitigate malicious events, this paper studied the Intrusion Detection Systems (IDS) based on Machine Learning (ML) techniques. The ML approach could provide good tools to detect novel intrusion activities in a timely manner. This paper, therefore, highlighted the related issues to develop secured and efficient IoT services. It tried to allow a comprehensive review of IoT features and design. It mainly focused on intrusion detection based on the machine learning schema and built a taxonomy of different IoT attacks and threats. This paper also compared between the different intrusion detection techniques and established a taxonomy of machine leaning methods for intrusion detection solutions.