An efficient self attention-based 1D-CNN-LSTM network for IoT attack detection and identification using network traffic

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DOI	Trouver le DOI : https://doi.org/10.1016/j.jiixd.2024.09.001
Auteur	Rechercher : Sasi, Tinshu Identifiant ORCID : https://orcid.org/0009-0007-2201-4760; Rechercher : Lashkari, Arash Habibi Identifiant ORCID : https://orcid.org/0000-0002-1240-6433; Rechercher : Lu, Rongxing Identifiant ORCID : https://orcid.org/0000-0001-5720-0941; Rechercher : Xiong, Pulei¹Identifiant ORCID : https://orcid.org/0000-0002-3460-6946; Rechercher : Iqbal, Shahrear¹Identifiant ORCID : https://orcid.org/0000-0001-7819-5715
Affiliation	Conseil national de recherches Canada. Technologies numériques
Format	Texte, Article
Sujet	IoT attacks; machine learning; artificial intelligence; IoT attack detection
Résumé	In the last ten years, the IoT has played a crucial role in society’s digital transformation. However, because of the wide range of devices it encompasses, it is also facing increased security vulnerabilities. This research presents a novel mechanism called the Self-attention-based 1D-CNN-LSTM, which uses Convolutional Neural Networks (CNNs) combined with a Long Short-Term Memory (LSTM) model enhanced with a Self-Attention mechanism for detecting IoT attacks. The proposed mechanism achieves high accuracy and efficiently differentiates malicious and benign network traffic. By employing Shapley Additive Explanations (SHAP), we identified important predictive features from the preprocessed data, which were retrieved using CICFlowmeter. This has strengthened the dependability of the model. In addition, we enhanced the model by training it on a smaller collection of features, resulting in shorter training time while preserving accuracy. We have also generated nine augmented IoT tabular datasets named CIC-BCCC-NRC_TabularIoTAttack-2024 from accessible IoT datasets to evaluate the model’s robustness and showcase its efficacy in IoT security.
Date de publication	2024-09-26
Maison d’édition	Elsevier
Licence	Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/deed.fr
Dans	Journal of Information and Intelligence, nº 5, 3 (26 septembre 2024) : 375–400.
Langue	anglais
Publications évaluées par des pairs	Oui
Identificateur	S2949715924000763
Exporter la notice	Exporter en format RIS
Signaler une correction	Signaler une correction (s'ouvre dans un nouvel onglet)
Identificateur de l’enregistrement	e8a12fbb-e01a-49e8-ae89-1afb014dd17d
Enregistrement créé	2024-10-22
Enregistrement modifié	2025-11-06

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