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Innovations and Interdisciplinary Solutions for Underserved Areas. 7th International Conference, InterSol 2024, Dakar, Senegal, July 3–4, 2024, Proceedings

Research Article

Towards the Implementation of a Dynamic IDS for IoT: Anomaly Detection in MQTT Traffic

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  • @INPROCEEDINGS{10.1007/978-3-031-86493-3_15,
        author={Abdoulaye Diallo and Lionel Affognon and Ch\^{e}rif Diallo and Eug\'{e}ne C. Ezin},
        title={Towards the Implementation of a Dynamic IDS for IoT: Anomaly Detection in MQTT Traffic},
        proceedings={Innovations and Interdisciplinary Solutions for Underserved Areas. 7th International Conference, InterSol 2024, Dakar, Senegal, July 3--4, 2024, Proceedings},
        proceedings_a={INTERSOL},
        year={2025},
        month={4},
        keywords={Internet of things intrusion detection system anomaly detection deep learning autoencoder},
        doi={10.1007/978-3-031-86493-3_15}
    }
    
  • Abdoulaye Diallo
    Lionel Affognon
    Chérif Diallo
    Eugène C. Ezin
    Year: 2025
    Towards the Implementation of a Dynamic IDS for IoT: Anomaly Detection in MQTT Traffic
    INTERSOL
    Springer
    DOI: 10.1007/978-3-031-86493-3_15
Abdoulaye Diallo1,*, Lionel Affognon2, Chérif Diallo1, Eugène C. Ezin2
  • 1: Department of Informatique, UFR des Sciences Appliquées et de Technologies
  • 2: Institut de Mathématiques et de Sciences Physiques (IMSP)
*Contact email: diallo.abdoulaye8@ugb.edu.sn

Abstract

The proliferation of IoT is evident in numerous domains today. Its applications are expanding rapidly, offering significant benefits. Nonetheless, it is plagued by numerous security flaws that can hinder its adoption in critical areas. This paper addresses the development of an intrusion detection system (IDS) for IoT networks. Specifically, we implement an autoencoder, an unsupervised deep learning model frequently employed in anomaly detection. The model is trained using the MQTTSet dataset, which contains both normal MQTT traffic and attack data. Training focused exclusively on legitimate data. Testing was conducted on both benign and malicious data to assess the model's effectiveness. The results indicate detection rates of 99.86% and 98.56% for normal and attack data, respectively.

Keywords
Internet of things intrusion detection system anomaly detection deep learning autoencoder
Published
2025-04-21
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-86493-3_15
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