A Review of recent IDS proposals based on Ensemble Learning in IoT Networks

This paper presents a review of recent Intrusion Detection Systems (IDSs) proposals based on Ensemble Learning (EL) in Internet of Things (IoT) networks, focusing on the most relevant studies published recently between 2021 and 2023. In this study, we aim to present a summary of the algorithms and d...

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Bibliographic Details
Published in2023 7th IEEE Congress on Information Science and Technology (CiSt) pp. 187 - 192
Main Authors Doumal, Zouhir, Fadili, Hakim El, Dosse, Saad Bennani
Format Conference Proceeding
LanguageEnglish
Published IEEE 16.12.2023
Subjects
Ensemble learning
Internet of Things
Intrusion Detection System
Machine Learning
Proposals
Random forests
Robustness
Security
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Summary:This paper presents a review of recent Intrusion Detection Systems (IDSs) proposals based on Ensemble Learning (EL) in Internet of Things (IoT) networks, focusing on the most relevant studies published recently between 2021 and 2023. In this study, we aim to present a summary of the algorithms and datasets used in developing these mechanisms. We also investigate whether researchers consider aspects of performance and attack detection speed when designing these types of IDS, given the limited resources in IoT networks. Overall, our study provides an overview of the current state of the use of Ensemble Learning in IDS and highlights areas requiring further research to advance the field.
ISSN:2327-1884
DOI:10.1109/CiSt56084.2023.10409870