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Resource efficient boosting method for IoT security monitoring.

Zakariyya, Idris; Al-Kadri, M. Omar; Kalutarage, Harsha

Authors

Idris Zakariyya

M. Omar Al-Kadri

Harsha Kalutarage



Abstract

Machine learning (ML) methods are widely proposed for security monitoring of Internet of Things (IoT). However, these methods can be computationally expensive for resource constraint IoT devices. This paper proposes an optimized resource efficient ML method that can detect various attacks on IoT devices. It utilizes Light Gradient Boosting Machine (LGBM). The performance of this approach was evaluated against four realistic IoT benchmark datasets. Experimental results show that the proposed method can effectively detect attacks on IoT devices with limited resources, and outperforms the state of the art techniques.

Citation

ZAKARIYYA, I., AL-KADRI, M.O. and KALUTARAGE, H. 2021. Resource efficient boosting method for IoT security monitoring. In Proceedings of 18th Institute of Electrical and Electronics Engineers (IEEE) Consumer communications and networking conference 2021 (CCNC 2021), 9-12 January 2021, Las Vegas, USA [virtual conference]. Piscataway: IEEE [online], article 9369620. Available from: https://doi.org/10.1109/ccnc49032.2021.9369620

Conference Name 18th Institute of Electrical and Electronics Engineers (IEEE) Consumer communications and networking conference 2021 (CCNC 2021)
Conference Location Las Vegas, USA [virtual conference]
Start Date Jan 9, 2021
End Date Jan 12, 2021
Acceptance Date Oct 16, 2020
Online Publication Date Jan 12, 2021
Publication Date Mar 11, 2021
Deposit Date Mar 23, 2021
Publicly Available Date Mar 23, 2021
Publisher Institute of Electrical and Electronics Engineers
Series ISSN 2331-9860
ISBN 9781728197944
DOI https://doi.org/10.1109/ccnc49032.2021.9369620
Keywords Machine learning; Internet of things; Resource constraint; Light gradient boosting machine
Public URL https://rgu-repository.worktribe.com/output/1279971

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