Critical review of network intrusion detection benchmark datasets for practical IoT security

Apejoye, O ORCID: https://orcid.org/0009-0008-3463-3320, Ajienka, N ORCID: https://orcid.org/0000-0002-8792-282X, He, J ORCID: https://orcid.org/0000-0002-5616-4691 and Ma, X ORCID: https://orcid.org/0000-0003-0074-4192, 2025. Critical review of network intrusion detection benchmark datasets for practical IoT security. Computer Networks and Communications, 3 (2), pp. 182-208. ISSN 2972-4619

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Abstract

The rapid expansion of Internet of Things (IoT) devices in modern environments introduces significant security vulnerabilities, increasing the risk of cyberattacks and potential physical threats to users. While Network Intrusion Detection Systems based on Machine Learning (ML-based NIDS) hold promise for mitigating these risks, the effectiveness of such systems is heavily influenced by the quality and representativeness of the datasets used for their development and evaluation. This study provides a critical review of publicly available benchmarking datasets commonly used to train ML-based NIDS for IoT security, with a particular focus on two pivotal but often overlooked factors: testbed configurations and feature extraction methods. The study's findings reveal critical inconsistencies in dataset design and feature sets, posing challenges to model generalizability and its real-world application. To address these issues, the study proposes clear criteria for dataset assessment and practical recommendations for researchers and dataset developers. The findings demonstrate the urgent need for standardisation to enhance reproducibility, enable fair comparison of intrusion detection models, and bridge the gap between academic research and practical IoT security solutions.

Item Type:	Journal article
Publication Title:	Computer Networks and Communications
Creators:	Apejoye, O., Ajienka, N., He, J. and Ma, X.
Publisher:	Universal Wiser Publisher Pte. Ltd
Date:	December 2025
Volume:	3
Number:	2
ISSN:	2972-4619
Identifiers:	Number Type 10.37256/cnc.3220257228 DOI 2621597 Other
Rights:	Copyright ©2025 Oluwasegun Apejoye, et al. This is an open-access article distributed under a CC BY license (Creative Commons Attribution 4.0 International License) https://creativecommons.org/licenses/by/4.0/
Divisions:	Schools > School of Science and Technology
Record created by:	Melissa Cornwell
Date Added:	27 Apr 2026 15:09
Last Modified:	27 Apr 2026 15:09
Related URLs:	Publisher
URI:	https://irep.ntu.ac.uk/id/eprint/55608

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