Identification and detection of DDoS attack on smart home infrastructure using machine learning models

Raja, TV, Ezziane, Z ORCID: https://orcid.org/0000-0002-7440-5356, He, J ORCID: https://orcid.org/0000-0002-5616-4691, Ma, X ORCID: https://orcid.org/0000-0003-0074-4192 and Kazaure, AW-Z, 2026. Identification and detection of DDoS attack on smart home infrastructure using machine learning models. Scientific Reports, 16: 2238. ISSN 2045-2322

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Abstract

This study investigates Distributed Denial-of-Service (DDoS) attack detection within smart home environments using both traditional machine learning and deep learning approaches. Real smart home traffic data, collected approximately 11.5 h of normal and attack activity, was used to implement and evaluate two models: k-Nearest Neighbour (k-NN) and an Artificial Neural Network (ANN). The k-NN model achieved an accuracy of 97.13%, while the ANN achieved 81.7% accuracy under the same dataset conditions. Unlike previous studies relying solely on benchmark datasets, this work uses self-collected smart home data to assess model feasibility and real-world deployment potential.

Item Type:	Journal article
Publication Title:	Scientific Reports
Creators:	Raja, T.V., Ezziane, Z., He, J., Ma, X. and Kazaure, A.W.-Z.
Publisher:	Springer Science and Business Media LLC
Date:	19 January 2026
Volume:	16
ISSN:	2045-2322
Identifiers:	Number Type 10.1038/s41598-025-32004-y DOI 2621629 Other
Rights:	© The Author(s) 2026. This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Divisions:	Schools > School of Science and Technology
Record created by:	Melissa Cornwell
Date Added:	27 Apr 2026 16:05
Last Modified:	27 Apr 2026 16:05
URI:	https://irep.ntu.ac.uk/id/eprint/55610

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