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VAIDS: A Hybrid Deep Learning Model to Detect Intrusions in MQTT Protocol Enabled Networks

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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2027))

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Abstract

The adoption of 5G networks is seen as an enabler for IoT. With low latency and increased reach, many more IoT devices can now be connected and controlled. An increase in the number of IoT devices since its inception brought forth the need for a lightweight communication protocol. The protocol in question should be able to cater to a large number of IoT devices. These requirements were the basis of the MQTT protocol. Attackers can utilize the protocol to target a network. Furthermore it is a herculean task to manually identify an attack in a huge network. Artificial Intelligence can be used to efficiently detect such attacks with a high degree of accuracy. In this research we propose a hybrid deep learning mult-iclass classification model VAIDS. VAIDS utilizes the CNN algorithm to extract features from the dataset. These features are then utilized as inputs for the LSTM algorithm. The proposed model can detect five types of anomalies within an IoT network that uses the MQTT protocol. The proposed model is trained, tested and validated against the MQTT-IoT-IDS2020 dataset and classifies a given input into one of five attack classes. The model showcases a high degree of accuracy of 99.97%.

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Authors and Affiliations

  1. Sardar Patel University of Police Security and Criminal Justice, Jodhpur, India

    Chetanya Kunndra, Arjun Choudhary & Prashant Mathur

  2. Indian Institute of Technology, Jodhpur, India

    Jaspreet Kaur

Authors
  1. Chetanya Kunndra
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  2. Arjun Choudhary
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  3. Jaspreet Kaur
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  4. Prashant Mathur
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Corresponding author

Correspondence to Arjun Choudhary .

Editor information

Editors and Affiliations

  1. University of South Dakota, Vermillion, SD, USA

    KC Santosh

  2. University of Derby, Derby, UK

    Aaisha Makkar

  3. University of Derby, Derby, UK

    Myra Conway

  4. Indian Institute of Information Technology, Bhopal, India

    Ashutosh K. Singh

  5. University of Clermont Auvergne, Aubière, France

    Antoine Vacavant

  6. ENSA - Cadi Ayyad University, Marrakesh, Morocco

    Anas Abou el Kalam

  7. Halmstad University, Halmstad, Sweden

    Mohamed-Rafik Bouguelia

  8. Central University of Karnataka, Kalaburagi, India

    Ravindra Hegadi

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Kunndra, C., Choudhary, A., Kaur, J., Mathur, P. (2024). VAIDS: A Hybrid Deep Learning Model to Detect Intrusions in MQTT Protocol Enabled Networks. In: Santosh, K., et al. Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2023. Communications in Computer and Information Science, vol 2027. Springer, Cham. https://doi.org/10.1007/978-3-031-53085-2_18

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  • DOI: https://doi.org/10.1007/978-3-031-53085-2_18

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  • Online ISBN: 978-3-031-53085-2

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