FlowPrint: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic

FlowPrint: полууправляемая идентификация мобильных приложений по зашифрованному сетевому трафику
Thijs van Ede, Riccardo Bortolameotti, Andrea Continella, Jingjing Ren, Daniel J. Dubois, Martina Lindorfer, David Choffnes, Maarten van Steen, Peter Andreas
2020-01-01

content delivery networksencrypted network trafficmobile environmentsmobile-app fingerprintingsemi-supervised learning
Mobile-application fingerprinting of network traffic is valuable for many security solutions as it provides insights into the apps active on a network. Unfortunately, existing techniques require prior knowledge of apps to be able to recognize them. However, mobile environments are constantly evolving, i.e., apps are regularly installed, updated, and uninstalled. Therefore, it is infeasible for existing fingerprinting approaches to cover all apps that may appear on a network. Moreover, most mobile traffic is encrypted, shows similarities with other apps, e.g., due to common libraries or the use of content delivery networks, and depends on user input, further complicating the fingerprinting process.
1
App installation, updates, and removal make it infeasible for conventional fingerprinting approaches to maintain coverage of every application appearing on a network.
2
Encrypted traffic, shared libraries, content delivery networks, and user-dependent behavior create substantial challenges for distinguishing mobile applications from network traces.
3
Existing mobile-app traffic fingerprinting methods require prior knowledge of applications, limiting recognition in dynamically changing mobile environments.
4
FlowPrint targets semi-supervised mobile-app fingerprinting to provide insights into active applications despite these evolving and ambiguous encrypted-traffic conditions.

encrypted network traffic generated by mobile applications

semi-supervised fingerprinting and recognition of mobile applications under evolving app populations, traffic encryption, inter-app similarities, and user-input variability

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Publication Date
2020-01-01
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Authors
Thijs van Ede
Riccardo Bortolameotti
Andrea Continella
Jingjing Ren
Daniel J. Dubois
Martina Lindorfer
David Choffnes
Maarten van Steen
Peter Andreas
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