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Title	Design Of Intrusion Detection System Using Ga And Cnn For Mqtt-Based Iot Networks
ID_Doc	18873
Authors	Dandapat A.; Mondal B.
Year	2024
Published	Wireless Personal Communications, 134, 4
DOI	http://dx.doi.org/10.1007/s11277-024-10984-w
Abstract	With the advancement of technology, Internet of Things (IoT) devices are integrated with smart homes, smart cities, intelligent medical systems, industries, smart cars, and many more applications to monitor and control them. These devices are connected to different heterogeneous environments and have many environmental constraints such as power, bandwidth, resources, etc. Unfortunately, this makes them attractive targets for attackers to perform malicious behaviours. It necessitates updating the current intrusion detection system (IDS) to cope with existing and new challenges. This paper proposes an IDS to secure a Message Queuing Telemetry Transport (MQTT)-based IoT environment. The MQTT does not use robust encryption algorithms to encrypt the transmitted data for fast communication, which makes the networks vulnerable to intruders and network attacks. The proposed model selects essential features using a genetic algorithm, and these selected features are used to train a convolutional neural network model for network packet classification. We have used the MQTT-IoT-IDS2020 dataset to analyze and measure the model’s performance. The test results are promising and prove that the proposed scheme can identify potential intrusions in the MQTT networks. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Author Keywords	Convolutional neural network; Deep learning; Internet of Things; Intrusion detection system; Message Queuing Telemetry Transport; Supervised learning

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