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Erschienen in:
2024 | OriginalPaper | Buchkapitel
An MQTT IoT Intrusion Detection System Using Deep-Learning
verfasst von : Greeshma Andrew, M. P. Deepika, Soumia Chandran
Erschienen in: Proceedings of Third International Conference on Computing and Communication Networks
Verlag: Springer Nature Singapore
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Abstract
Nowadays the devices connected to an Internet of Things (IoT) network is enormously increasing day by day. IoT devices widely using Message Queuing Telemetry Transport (MQTT) protocol for their communication. Because of the heterogeneous nature and lack of security in IoT devices manufacturing, security systems against MQTT traffic are essential. In this paper, we proposed a zero biased Convolutional Neural Network (CNN) for the detection of intrusion. By removing the bias term, it reduces the computational complexity and it would be beneficial to deployed as Intrusion Detection System (IDS) for resource constrained IoT devices. The performance of binary classification of the model is compared with other Deep Neural Network (DNN) with the help of two abstract level features such as bidirectional flow and unidirectional flow from a publicly available dataset MQTT-IoT-IDS2020. The proposed model achieves superior results to the other model with the F1 score of 0.99 for bidirectional and unidirectional flow.