A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT
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...
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MDPI AG
2021-10-01
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| Online Access: | https://www.mdpi.com/1424-8220/21/21/7016 |
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| author | Muhammad Almas Khan Muazzam A. Khan Sana Ullah Jan Jawad Ahmad Sajjad Shaukat Jamal Awais Aziz Shah Nikolaos Pitropakis William J. Buchanan |
| author_facet | Muhammad Almas Khan Muazzam A. Khan Sana Ullah Jan Jawad Ahmad Sajjad Shaukat Jamal Awais Aziz Shah Nikolaos Pitropakis William J. Buchanan |
| author_sort | Muhammad Almas Khan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T05:52:56Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:52:56Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6ffba6c808314d33a4c1320a4aa27ea12023-11-22T21:35:19ZengMDPI AGSensors1424-82202021-10-012121701610.3390/s21217016A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoTMuhammad Almas Khan0Muazzam A. Khan1Sana Ullah Jan2Jawad Ahmad3Sajjad Shaukat Jamal4Awais Aziz Shah5Nikolaos Pitropakis6William J. Buchanan7Department of Computer Sciences, Quaid-i-Azam University, Islamabad 44000, PakistanDepartment of Computer Sciences, Quaid-i-Azam University, Islamabad 44000, PakistanSchool of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UKSchool of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UKDepartment of Mathematics, College of Science, King Khalid University, Abha 61413, Saudi ArabiaDepartment of Electrical and Informational Engineering (DEI), Polytechnic University of Bari, 70125 Bari, ItalySchool of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UKSchool of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UKA 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.https://www.mdpi.com/1424-8220/21/21/7016MQTTIDSIoTsecurityclassification |
| spellingShingle | Muhammad Almas Khan Muazzam A. Khan Sana Ullah Jan Jawad Ahmad Sajjad Shaukat Jamal Awais Aziz Shah Nikolaos Pitropakis William J. Buchanan A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT Sensors MQTT IDS IoT security classification |
| title | A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT |
| title_full | A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT |
| title_fullStr | A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT |
| title_full_unstemmed | A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT |
| title_short | A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT |
| title_sort | deep learning based intrusion detection system for mqtt enabled iot |
| topic | MQTT IDS IoT security classification |
| url | https://www.mdpi.com/1424-8220/21/21/7016 |
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