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Conference Papers Year : 2020

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 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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Dates and versions

hal-04060656 , version 1 (06-04-2023)





Enkhtur Tsogbaatar, Monowar H. Bhuyan, Yuzo Taenaka, Doudou Fall, Khishigjargal Gonchigsumlaa, et al.. SDN-Enabled IoT Anomaly Detection Using Ensemble Learning. 16th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2020, Neos Marmaras, Greece. pp.268-280, ⟨10.1007/978-3-030-49186-4_23⟩. ⟨hal-04060656⟩
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