MEML: Resource-aware MQTT-based Machine Learning for Network Attacks Detection on IoT Edge Devices

Andrii Shalaginov, Oleksandr Semeniuta, Mamoun Alazab

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedings

Abstract

Growing number of Smart Applications in recent years bring a completely new landscape of cyber-attacks and exploitation scenario that have not been seen in wild before. Devices in Edge commonly have very limited computational resources and corresponding power source reducing the number of conventional cybersecurity measures available for deployment. This also puts strict requirements on how the signatures of malicious actions can be updated and actualized. It has been proved efficiency of Machine Learning models, Neural Networks in particular, in multiple tasks related to cybersecurity due to the high-abstract precise models and training from historical data. However, when it comes to the devices in Edge, it is clear that the extensive training of the model is not possible, while testing of new unseen data can be successfully done. In addition to the conventional understanding of off-line and on-line model training, this contribution looks into how the Machine Learning can be successfully deployed on IoT while putting unnecessary computations off-chip through parameters transfer over MQTT network, reducing computational footprint on micro-controllers. We believe that proposed approach will be beneficial for many applications in resource-constrained environment.
Original languageEnglish
Title of host publicationUCC 2019 Companion - Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing
PublisherAssociation for Computing Machinery, Inc
Pages123-128
Number of pages6
ISBN (Electronic)9781450370448
ISBN (Print)9781450370448
DOIs
Publication statusPublished - 2 Dec 2019
Event12th IEEE/ACM International Conference on Utility and Cloud Computing, UCC Companion 2019 - Auckland, New Zealand
Duration: 2 Dec 20195 Dec 2019

Publication series

NameUCC 2019 Companion - Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing

Conference

Conference12th IEEE/ACM International Conference on Utility and Cloud Computing, UCC Companion 2019
CountryNew Zealand
CityAuckland
Period2/12/195/12/19

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  • Cite this

    Shalaginov, A., Semeniuta, O., & Alazab, M. (2019). MEML: Resource-aware MQTT-based Machine Learning for Network Attacks Detection on IoT Edge Devices. In UCC 2019 Companion - Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing (pp. 123-128). (UCC 2019 Companion - Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing). Association for Computing Machinery, Inc. https://doi.org/10.1145/3368235.3368876