Clustering Unknown IoT Devices in a 5G Mobile Network Security Context via Machine Learning

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openAccess
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Conference article in proceedings
Date
2021-11-22
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Language
en
Pages
6
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2021 17th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), IEEE International Conference on Wireless and Mobile Computing, Networking, and Communications
Abstract
We propose a novel machine-learning pipeline for clustering unknown IoT devices in an industrial 5G mobile-network setting. Organizing IoT devices as few homogeneous device groups improves the applicability of network-intrusion detection systems. More specifically, we develop feature engineering methods that transform IP-flows into device-level data points, define distance metrics between the data points, and apply the DBSCAN algorithm on them. Our experiments on a simulated IoT device network with varying levels of noise show that our proposed methodology outperforms alternative methods and is the only one producing a robust grouping of the IoT devices with noise present in the traffic data.
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Hämmäinen , T & Kahles , J 2021 , Clustering Unknown IoT Devices in a 5G Mobile Network Security Context via Machine Learning . in 2021 17th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) . IEEE International Conference on Wireless and Mobile Computing, Networking, and Communications , IEEE , IEEE International Conference on Wireless and Mobile Computing, Networking and Communications , Bologna , Italy , 11/10/2021 . https://doi.org/10.1109/WiMob52687.2021.9606307