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CNN for user activity detection using encrypted in-app mobile data

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journal contribution
posted on 22.02.2022, 11:25 authored by Omattage Madushi H. Pathmaperuma, Yogachandran RahulamathavanYogachandran Rahulamathavan, Safak DoganSafak Dogan, Ahmet Kondoz
In this study, a simple yet effective framework is proposed to characterize fine grained in-app user activities performed on mobile applications using Convolutional Neural Network (CNN). The proposed framework uses a time window-based approach to split the activity’s encrypted traffic flow into segments, so that in-app activities can be identified just by observing only a part of the activity related encrypted traffic. In this study matrices are constructed for each encrypted traffic flow segment. These matrices act as input to the CNN model, allowing it to learn to differentiate previously trained (known) and previously untrained (unknown) in-app activities, as well as the known in-app activity type. The proposed method extracts and selects salient features for encrypted traffic classification. This is the first known approach proposing to filter unknown traffic with an average accuracy of 88%. Once the unknown traffic is filtered, the classification accuracy of our model would be 92%.

Funding

Loughborough University, UK

HappierFeet-Disrupting the vicious cycle of healthcare decline in Diabetic Foot Ulceration through active prevention: The future of self-managed care

Engineering and Physical Sciences Research Council

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History

School

  • Loughborough University London

Published in

Future Internet

Volume

14

Issue

2

Publisher

MDPI AG

Version

VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by MDPI under the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

15/02/2022

Publication date

2022-02-21

Copyright date

2022

ISSN

1999-5903

Language

en

Depositor

Dr Safak Dogan. Deposit date: 17 February 2022

Article number

67