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The Internet of Things (IoT) is one of the main research fields in theCybersecurity domain. This is due to (a) the increased dependency on automateddevice, and (b) the inadequacy of general purpose Intrusion Detection Systems(IDS) to be deployed for special purpose networks usage. Numerous lightweightprotocols are being proposed for IoT devices communication usage. One of thedistinguishable IoT machine-to-machine communication protocols is MessageQueuing Telemetry Transport (MQTT) protocol. However, as per the authors bestknowledge, there are no available IDS datasets that include MQTT benign orattack instances and thus, no IDS experimental results available. In thispaper, the effectiveness of six Machine Learning (ML) techniques to detectMQTT-based attacks is evaluated. Three abstraction levels of features areassessed, namely, packet-based, unidirectional flow, and bidirectional flowfeatures. An MQTT simulated dataset is generated and used for the training andevaluation processes. The dataset is released with an open access licence tohelp the research community further analyse the accompanied challenges. Theexperimental results demonstrated the adequacy of the proposed ML models tosuit MQTT-based networks IDS requirements. Moreover, the results emphasise onthe importance of using flow-based features to discriminate MQTT-based attacksfrom benign traffic, while packet-based features are sufficient for traditionalnetworking attacks.

Machine Learning Based IoT Intrusion Detection System: An MQTT Case Study (MQTT-IoT-IDS2020 Dataset)