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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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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. |
format | Online Article Text |
id | pubmed-7256579 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
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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