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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Witten IH, Frank E, Hall MA, Pal CJ (2016) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, Cambridge
Grimaudo L, Mellia M, Baralis E (2012) Hierarchical learning for fine grained internet traffic classification. In: Proceedings of IWCMC, Aug 2012
Fontugne R et al (2010) Mawilab: Combining diverse anomaly detectors for automated anomaly labeling and performance benchmarking. In: ACM CoNEXT10, Dec 2010
Sivanathan A, Habibi Gharakheili H, Loi F, Radford A, Wijenayake C, Vishwanath A, Sivaraman V (2018) Classifying IoT devices in smart environments using network traffic characteristics. IEEE Trans Mob Comput
Shafiq MZ, Ji L, Liu AX, Pang J, Wang J (2013) Large-scale measurement and characterization of cellular machine-to-machine traffic. IEEE/ACM Trans Netw 21(6):1960–1973
Grimaudo L, Mellia M, Baralis E, Keralapura R (2014) SeLeCT: self-learning classifier for internet traffic. IEEE Trans Netw Serv Manage 11(2):144–157
Pant V, Passerone R, Welponer M, Rizzon L, Lavagnolo R (2017) Efficient neural computation on network processors for IoT protocol classification. In: Proceedings of the first new generation of circuits and systems conference, NGCAS 2017, Genova, Italy, 7–9 Sept 2017
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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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