The Internet of Things (IoT) is one of the main research fields in the Cybersecurity domain. This is due to (a) the increased dependency on automated device, and (b) the inadequacy of general-purpose Intrusion Detection Systems (IDS) to be deployed for special purpose networks usage. Numerous lightweight protocols are being proposed for IoT devices communication usage. One of the distinguishable IoT machine-to-machine communication protocols is Message Queuing Telemetry Transport (MQTT) protocol. However, as per the authors best knowledge, there are no available IDS datasets that include MQTT benign or attack instances and thus, no IDS experimental results available. In this paper, the effectiveness of six Machine Learning (ML) techniques to detect MQTT-based attacks is evaluated. Three abstraction levels of features are assessed, namely, packet-based, unidirectional flow, and bidirectional flow features. An MQTT simulated dataset is generated and used for the training and evaluation processes. The dataset is released with an open access licence to help the research community further analyse the accompanied challenges. The experimental results demonstrated the adequacy of the proposed ML models to suit MQTT-based networks IDS requirements. Moreover, the results emphasise on the importance of using flow-based features to discriminate MQTT-based attacks from benign traffic, while packet-based features are sufficient for traditional networking attacks.
|Title of host publication||Selected Papers from the 12th International Networking Conference, INC 2020|
|Editors||Bogdan Ghita, Stavros Shiaeles|
|Place of Publication||Cham|
|Number of pages||12|
|Publication status||Published - 5 Jan 2021|
|Event||12th International Network Conference 2020 - Virtual conference, Rhodes, Greece|
Duration: 19 Sep 2020 → 21 Sep 2020
Conference number: 12th
|Name||Lecture Notes in Networks and Systems, LNNS|
|Conference||12th International Network Conference 2020|
|Period||19/09/20 → 21/09/20|
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