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3. DeL-IoT: A Deep Ensemble Learning Approach to Uncover Anomalies in IoT

Author

Tsogbaatar, E., Bhuyan, Monowar H., Taenaka, Y., Fall, D., Elmroth, Erik, Kadobayashi, Y., Tsogbaatar, E., Bhuyan, Monowar H., Taenaka, Y., Fall, D., Elmroth, Erik, and Kadobayashi, Y.

Abstract

Internet of Things (IoT) devices are inherently vulnerable due to insecure design, implementation, and configuration. Aggressive behavior changes, due to increased attacker’s sophistication, and the heterogeneity of the data in IoT have proven that securing IoT devices trigger multiple challenges. It includes complex and dynamic attack detection, data imbalance, data heterogeneity, real-time response, and prediction capability. Most researchers are not focusing on the class imbalance, dynamic attack detection, and data heterogeneity problems together in Software-Defined Networking (SDN) enabled IoT anomaly detection. Thus, to address these challenging tasks, we propose DeL-IoT, a deep ensemble learning framework for IoT anomaly detection and prediction using SDN, having three primary modules including anomaly detection, intelligent flow management, and device status forecasting. The DeL-IoT employs deep and stacked autoencoders to extract handy features for stacking into an ensemble learning model. This framework yields efficient detection of anomalies, manages flows dynamically, and forecasts both short and long-term device status for early action. We validate the proposed DeL-IoT framework with testbed and benchmark datasets. We demonstrate that in even a 1% imbalanced dataset, the performance of our proposed method, deep feature extraction with a deep ensemble learning model, is around 3% better than the single model. The extensive experimental results show that our models have a better and more reliable performance than the competing models showcased in the relevant related work.

Published
2021
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38. Long-Term in Vivo Testing of the National Cardiovascular Center Electrohydraulic Total Artificial Heart

Author

Tatsumi, E, primary, Taenaka, Y, additional, Uesho, K, additional, Homma, A, additional, Nishinaka, T, additional, Kakuta, Y, additional, Tsukiya, T, additional, Katagiri, N, additional, Shioya, K, additional, Takahashi, M, additional, Ohnishi, H, additional, Sato, K, additional, Takano, H, additional, Masuzawa, T, additional, Nakamura, M, additional, Tsukahara, K, additional, and Tsuchimoto, K, additional

Published
2001
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