University of Oulu

Mohammad A. Hoque, Benjamin Finley, Ashwin Rao, Abhishek Kumar, Pan Hui, Mostafa Ammar, Sasu Tarkoma, Context-driven encrypted multimedia traffic classification on mobile devices, Pervasive and Mobile Computing, Volume 88, 2022, 101737, ISSN 1574-1192,

Context-driven encrypted multimedia traffic classification on mobile devices

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Author: Hoque, Mohammad A.1; Finley, Benjamin1; Rao, Ashwin1;
Organizations: 1University of Helsinki, Helsinki, Finland
2University of Oulu, Oulu, Finland
3The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China
4The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region
5Georgia Institute of Technology, Atlanta, USA
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.7 MB)
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Language: English
Published: Elsevier, 2022
Publish Date: 2023-01-04


The Internet has been experiencing immense growth in multimedia traffic from mobile devices. The increase in traffic presents many challenges to user-centric networks, network operators, and service providers. Foremost among these challenges is the inability of networks to determine the types of encrypted traffic and thus the level of network service the traffic needs to maintain an acceptable quality of experience. Therefore, end devices are a natural fit for performing traffic classification since end devices have more contextual information about device usage and traffic. This paper proposes a novel approach that classifies multimedia traffic types produced and consumed on mobile devices. The technique relies on a mobile device’s detection of its multimedia context characterized by its utilization of different media input/output (I/O) components, e.g., camera, microphone, and speaker. We develop an algorithm, MediaSense, which senses the states of multiple I/O components and identifies the specific multimedia context of a mobile device in real-time. We demonstrate that MediaSense classifies encrypted multimedia traffic in real-time as accurately as deep learning approaches and with even better generalizability.

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Series: Pervasive and mobile computing
ISSN: 1574-1192
ISSN-E: 1873-1589
ISSN-L: 1574-1192
Volume: 88
Article number: 101737
DOI: 10.1016/j.pmcj.2022.101737
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: The work was supported by the Academy of Finland, Finland IDEA-MILL project (Grant Number 335934), Academy of Finland, Finland 5GEAR project (Grant Number 319669), Nokia Foundation, Finland Grant, Walter Ahlström Foundation, Finland Grant, Academy of Finland, Finland FIT project (Grant Number 325570), Academy of Finland, Finland 6G Flagship Program (Grant Number 346208), and GP INDFICORE, Finland EFFICACY Menot (Grant Number 24650101115). Mostafa Ammar’s work was partially supported by NSF, USA grant NETS (Grant Number 1909040).
Academy of Finland Grant Number: 346208
Detailed Information: 346208 (Academy of Finland Funding decision)
Copyright information: © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (