Resource efficient boosting method for IoT security monitoring.
Zakariyya, Idris; Al-Kadri, M. Omar; Kalutarage, Harsha
M. Omar Al-Kadri
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.
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|
|Keywords||Machine learning; Internet of things; Resource constraint; Light gradient boosting machine|
ZAKARIYYA 2021 Resource efficient
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