Dynamic learning framework for IoT intrusion detection using statistical approach and unsupervised learning.

Dynamic learning framework for IoT intrusion detection using statistical approach and unsupervised learning. | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 11 March 2025 V1 Latest version Share on Dynamic learning framework for IoT intrusion detection using statistical approach and unsupervised learning. Authors : Mohamed Khalafalla Hassan 0000-0002-6238-8915 [email protected] , Sharifah Hafizah Sayed Ariffin , Mutaz Hamad , Safa Elhadi , and Bushra Mohammed Ali Abdalla Authors Info & Affiliations https://doi.org/10.22541/au.174168329.96264496/v1 302 views 159 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The Internet of Things (IoT) is increasingly becoming integral in various sectors like transportation and healthcare, driving the development of new services. This paper proposes an innovative security approach for IoT, utilizing feature selection, dynamic learning with statistical change detection, and Automated Machine Learning (AutoML) for ongoing model refinement. Applied to a comprehensive IoT dataset, this method effectively tackles feature drift in dynamic environments, underscoring the need for flexible cybersecurity tactics. It demonstrates the proposed framework’s role in transforming attack detection and classification in IoT. The testing involved 33 attacks on an IoT network with 105 devices. The results indicate that our methodology significantly improves classification performance by 8% to 67%, depending on the drift percentage. Supplementary Material File (dynamic learning framework for iot intrusion detection.docx) Download 571.73 KB Information & Authors Information Version history V1 Version 1 11 March 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords feature drift incremental learning internet of things intrusion detection system unsupervised learning Authors Affiliations Mohamed Khalafalla Hassan 0000-0002-6238-8915 [email protected] Universite de Moncton Faculte d'ingenierie View all articles by this author Sharifah Hafizah Sayed Ariffin Universiti Teknologi Malaysia School of Electrical Engineering View all articles by this author Mutaz Hamad Future University Faculty of Engineering View all articles by this author Safa Elhadi Sudan International University View all articles by this author Bushra Mohammed Ali Abdalla Ibn Sina University View all articles by this author Metrics & Citations Metrics Article Usage 302 views 159 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Mohamed Khalafalla Hassan, Sharifah Hafizah Sayed Ariffin, Mutaz Hamad, et al. Dynamic learning framework for IoT intrusion detection using statistical approach and unsupervised learning.. Authorea . 11 March 2025. 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