Journal article Open Access

Mobile Traffic Classification through Physical Control Channel Fingerprinting: a Deep Learning Approach

Trinh, Hoang Duy; Fernández Gambin, Ángel; Giupponi, Lorenza; Rossi, Michele; Dini, Paolo

The automatic classification of applications and services is an invaluable feature for new generation mobile networks. Here, we propose and validate algorithms to perform this task, at runtime, from the raw physical control channel of an operative mobile network, without having to decode and/or decrypt the transmitted flows. Towards this, we decode Downlink Control Information (DCI) messages carried within the LTE Physical Downlink Control CHannel (PDCCH). DCI messages are sent by the radio cell in clear text and, in this paper, are utilized to classify the applications and services executed at the connected mobile terminals. Two datasets are collected through a large measurement campaign: one labeled, used to train the classification algorithms, and one unlabeled, collected from four radio cells in the metropolitan area of Barcelona, in Spain. Among other approaches, our Convolutional Neural Network (CNN) classifier provides the highest classification accuracy of 98%. The CNN classifier is then augmented with the capability of rejecting sessions whose patterns do not conform to those learned during the training phase, and is subsequently utilized to attain a fine grained decomposition of the traffic for the four monitored radio cells, in an online and unsupervised fashion.

Grant numbers : 5G-REFINE - Resource EfFIcient 5G NEtworks (TEC2017-88373-R) and SCAVENGE - Sustainable CellulAr networks harVEstiNG ambient Energy (01 February 2016 - 31 March 2020) (H2020-MSCA-ITN-2015 675891) projects.© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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