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.
Statistical flow classification for the IoT / Cirillo, Gennaro; Passerone, Roberto; Posenato, Antonio; Rizzon, Luca. - 627:(2020), pp. 73-79. ( International Conference on Applications in Electronics Pervading Industry, Environment and Society, ApplePies 2019 Pisa, Italy 11-13 september 2019) [10.1007/978-3-030-37277-4_9].
Statistical flow classification for the IoT
Roberto Passerone;Luca Rizzon
2020-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione
