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A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT

Almas Khan, Muhammad; Khan, Muazzam A; Ullah Jan, Sana; Ahmad, Jawad; Jamal, Sajjad Shaukat; Shah, Awais Aziz; Pitropakis, Nikolaos; Buchanan, William J; Alonistioti, Nancy; Panagiotakis, Spyros; Markakis, Evangelos K

Authors

Muhammad Almas Khan

Muazzam A Khan

Sajjad Shaukat Jamal

Awais Aziz Shah

Nancy Alonistioti

Spyros Panagiotakis

Evangelos K Markakis



Abstract

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.

Citation

Almas Khan, M., Khan, M. A., Ullah Jan, S., Ahmad, J., Jamal, S. S., Shah, A. A., …Markakis, E. K. (2021). A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT. Sensors, 21(21), Article 7016. https://doi.org/10.3390/s21217016

Journal Article Type Article
Acceptance Date Oct 19, 2021
Online Publication Date Oct 22, 2021
Publication Date 2021
Deposit Date Oct 22, 2021
Publicly Available Date Mar 29, 2024
Journal Sensors
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 21
Issue 21
Article Number 7016
DOI https://doi.org/10.3390/s21217016
Keywords MQTT; IDS; IoT; security; classification
Public URL http://researchrepository.napier.ac.uk/Output/2815344
Publisher URL https://www.mdpi.com/1424-8220/21/21/7016

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A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT (1.7 Mb)
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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.








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