Abstract
The objective of this work is to analyze packet flows and classify them as traffic that belongs to IoT devices or to traditional non-IoT communication. We employ two methods: a clustering approach, which learns directly from the structure of the dataset, and a classification tree, trained with the collected data and evaluated using 10-fold cross validation. The results show that classification trees outperform clustering on all datasets, and achieve high accuracy on both homogeneous simulated and real deployment traffic data.
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Cirillo, G., Passerone, R., Posenato, A., Rizzon, L. (2020). Statistical Flow Classification for the IoT. In: Saponara, S., De Gloria, A. (eds) Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2019. Lecture Notes in Electrical Engineering, vol 627. Springer, Cham. https://doi.org/10.1007/978-3-030-37277-4_9
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DOI: https://doi.org/10.1007/978-3-030-37277-4_9
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