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SDN-Enabled IoT Anomaly Detection Using Ensemble Learning

Internet of Things (IoT) devices are inherently vulnerable due to insecure design, implementation, and configuration. Aggressive behavior change, due to increased attacker’s sophistication, and the heterogeneity of the data in IoT have proven that securing IoT devices is a making challenge. To detec...

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Autores principales: Tsogbaatar, Enkhtur, Bhuyan, Monowar H., Taenaka, Yuzo, Fall, Doudou, Gonchigsumlaa, Khishigjargal, Elmroth, Erik, Kadobayashi, Youki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256579/
http://dx.doi.org/10.1007/978-3-030-49186-4_23
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author Tsogbaatar, Enkhtur
Bhuyan, Monowar H.
Taenaka, Yuzo
Fall, Doudou
Gonchigsumlaa, Khishigjargal
Elmroth, Erik
Kadobayashi, Youki
author_facet Tsogbaatar, Enkhtur
Bhuyan, Monowar H.
Taenaka, Yuzo
Fall, Doudou
Gonchigsumlaa, Khishigjargal
Elmroth, Erik
Kadobayashi, Youki
author_sort Tsogbaatar, Enkhtur
collection PubMed
description Internet of Things (IoT) devices are inherently vulnerable due to insecure design, implementation, and configuration. Aggressive behavior change, due to increased attacker’s sophistication, and the heterogeneity of the data in IoT have proven that securing IoT devices is a making challenge. To detect intensive attacks and increase device uptime, we propose a novel ensemble learning model for IoT anomaly detection using software-defined networks (SDN). We use a deep auto-encoder to extract handy features for stacking into an ensemble learning model. The learned model is deployed in the SDN controller to detect anomalies or dynamic attacks in IoT by addressing the class imbalance problem. We validate the model with real-time testbed and benchmark datasets. The initial results show that our model has a better and more reliable performance than the competing models showcased in the relevant related work.
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spelling pubmed-72565792020-05-29 SDN-Enabled IoT Anomaly Detection Using Ensemble Learning Tsogbaatar, Enkhtur Bhuyan, Monowar H. Taenaka, Yuzo Fall, Doudou Gonchigsumlaa, Khishigjargal Elmroth, Erik Kadobayashi, Youki Artificial Intelligence Applications and Innovations Article Internet of Things (IoT) devices are inherently vulnerable due to insecure design, implementation, and configuration. Aggressive behavior change, due to increased attacker’s sophistication, and the heterogeneity of the data in IoT have proven that securing IoT devices is a making challenge. To detect intensive attacks and increase device uptime, we propose a novel ensemble learning model for IoT anomaly detection using software-defined networks (SDN). We use a deep auto-encoder to extract handy features for stacking into an ensemble learning model. The learned model is deployed in the SDN controller to detect anomalies or dynamic attacks in IoT by addressing the class imbalance problem. We validate the model with real-time testbed and benchmark datasets. The initial results show that our model has a better and more reliable performance than the competing models showcased in the relevant related work. 2020-05-06 /pmc/articles/PMC7256579/ http://dx.doi.org/10.1007/978-3-030-49186-4_23 Text en © IFIP International Federation for Information Processing 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Tsogbaatar, Enkhtur
Bhuyan, Monowar H.
Taenaka, Yuzo
Fall, Doudou
Gonchigsumlaa, Khishigjargal
Elmroth, Erik
Kadobayashi, Youki
SDN-Enabled IoT Anomaly Detection Using Ensemble Learning
title SDN-Enabled IoT Anomaly Detection Using Ensemble Learning
title_full SDN-Enabled IoT Anomaly Detection Using Ensemble Learning
title_fullStr SDN-Enabled IoT Anomaly Detection Using Ensemble Learning
title_full_unstemmed SDN-Enabled IoT Anomaly Detection Using Ensemble Learning
title_short SDN-Enabled IoT Anomaly Detection Using Ensemble Learning
title_sort sdn-enabled iot anomaly detection using ensemble learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256579/
http://dx.doi.org/10.1007/978-3-030-49186-4_23
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