Document Type

Journal Article

Publication Title

Journal of Information and Telecommunication

Publisher

Taylor & Francis

School

School of Science / ECU Security Research Institute

RAS ID

31772

Comments

Syed, N. F., Baig, Z., Ibrahim, A., & Valli, C. (2020). Denial of service attack detection through machine learning for the IoT. Journal of Information and Telecommunication. Advance online publication. https://doi.org/10.1080/24751839.2020.1767484

Abstract

Sustained Internet of Things (IoT) deployment and functioning are heavily reliant on the use of effective data communication protocols. In the IoT landscape, the publish/subscribe-based Message Queuing Telemetry Transport (MQTT) protocol is popular. Cyber security threats against the MQTT protocol are anticipated to increase at par with its increasing use by IoT manufacturers. In particular, IoT is vulnerable to protocol-based Application layer Denial of Service (DoS) attacks, which have been known to cause widespread service disruption in legacy systems. In this paper, we propose an Application layer DoS attack detection framework for the MQTT protocol and test the scheme on legitimate and protocol compliant DoS attack scenarios. To protect the MQTT message brokers from such attacks, we propose a machine learning-based detection framework developed for the MQTT protocol. Through experiments, we demonstrate the impact of such attacks on various MQTT brokers and evaluate the effectiveness of the proposed framework to detect these malicious attacks. The results obtained indicate that the attackers can overwhelm the server resources even when legitimate access was denied to MQTT brokers and resources have been restricted. In addition, the MQTT features we have identified showed high attack detection accuracy. The field size and length-based features drastically reduced the false-positive rates and are suitable in detecting IoT based attacks.

DOI

10.1080/24751839.2020.1767484

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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