A deep learning-based intrusion detection system for MQTT enabled IoT

Khan, M. A., Khan, M. A., Jan, S. U., Ahmad, J., Jamal, S. S., Shah, A. A. , Pitropakis, N. and Buchanan, W. J. (2021) A deep learning-based intrusion detection system for MQTT enabled IoT. Sensors, 21(21), 7016. (doi: 10.3390/s21217016) (PMID:34770322) (PMCID:PMC8588509)

[img] Text
294669.pdf - Published Version
Available under License Creative Commons Attribution.



A large number of smart devices in Internet of Things (IoT) environments communicate via different messaging protocols. Message Queuing Telemetry Transport (MQTT) is a widely used publish–subscribe-based protocol for the communication of sensor or event data. The publish–subscribe strategy makes it more attractive for intruders and thus increases the number of possible attacks over MQTT. In this paper, we proposed a Deep Neural Network (DNN) for intrusion detection in the MQTT-based protocol and also compared its performance with other traditional machine learning (ML) algorithms, such as a Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbour (kNN), Decision Tree (DT), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs). The performance is proved using two different publicly available datasets, including (1) MQTT-IoT-IDS2020 and (2) a dataset with three different types of attacks, such as Man in the Middle (MitM), Intrusion in the network, and Denial of Services (DoS). The MQTT-IoT-IDS2020 contains three abstract-level features, including Uni-Flow, Bi-Flow, and Packet-Flow. The results for the first dataset and binary classification show that the DNN-based model achieved 99.92%, 99.75%, and 94.94% accuracies for Uni-flow, Bi-flow, and Packet-flow, respectively. However, in the case of multi-label classification, these accuracies reduced to 97.08%, 98.12%, and 90.79%, respectively. On the other hand, the proposed DNN model attains the highest accuracy of 97.13% against LSTM and GRUs for the second dataset.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Shah, Dr Awais Aziz
Authors: Khan, M. A., Khan, M. A., Jan, S. U., Ahmad, J., Jamal, S. S., Shah, A. A., Pitropakis, N., and Buchanan, W. J.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Sensors
ISSN (Online):Shaukat
Published Online:22 October 2021
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in Sensors 21(21): 7016
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record