@inproceedings { , title = {Resource efficient boosting method for IoT security monitoring.}, 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.}, conference = {18th Institute of Electrical and Electronics Engineers (IEEE) Consumer communications and networking conference 2021 (CCNC 2021)}, doi = {10.1109/ccnc49032.2021.9369620}, isbn = {9781728197944}, note = {INFO COMPLETE (Info via IEEE alert 19/3/2021 LM) PERMISSION GRANTED (version = AAM; embargo = none; licence = Pub's own; POLICY = https://www.ieee.org/publications/rights/rights-policies.html ) DOCUMENT READY (AAM rec'd) ADDITIONAL INFO - Contact: Idris Zakariyya; Omar Al Kadri; Harsha Kalutarage (Set Statement - © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.)}, publicationstatus = {Published}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, url = {https://rgu-repository.worktribe.com/output/1279971}, keyword = {Machine learning, Internet of things, Resource constraint, Light gradient boosting machine}, year = {2021}, author = {Zakariyya, Idris and Al-Kadri, M. Omar and Kalutarage, Harsha} }