Abstract
The Message Queuing Telemetry Transport protocol (MQTT) is one of the most widely used application protocols to facilitate machine-to-machine communication in an IoT environment. MQTT was built upon TCP/IP protocol and requires minimal resources since it is lightweight and efficient which makes it suitable for both domestic and industrial applications. However, the popularity and openness of this protocol make it vulnerable and exposed to a variety of assaults, including Denial of Service (DoS) attacks that can severely affect healthcare or manufacturing services. Thus, securing MQTT-based systems needs to develop a novel, effective, and adaptive intrusion detection approach. In this paper, we focus on MQTT’s flaws that allow hackers to take control of MQTT devices. We investigate a deep learning-based intrusion detection system to identify malicious behavior during communication between IoT devices, using an open source dataset namely ‘MQTT dataset’. The findings demonstrate that the suggested approach is more accurate than traditional approaches based on machine learning; with an accuracy rate greater than 99% and an F1-score greater than 98%.
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Zeghida, H., Boulaiche, M., Chikh, R. (2024). Detection of DoS Attacks in MQTT Environment. In: Bennour, A., Bouridane, A., Chaari, L. (eds) Intelligent Systems and Pattern Recognition. ISPR 2023. Communications in Computer and Information Science, vol 1941. Springer, Cham. https://doi.org/10.1007/978-3-031-46338-9_10
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DOI: https://doi.org/10.1007/978-3-031-46338-9_10
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