Mobile App Fingerprinting through Automata Learning and Machine Learning

Fatemeh Marzani, Fatemeh Ghassemi, Zeynab Sabahi-Kaviani, Thijs van Ede, Maarten van Steen

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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Abstract

Application fingerprinting is crucial in network management and security to provide the best Quality of Service (QoS). To generate fingerprints for applications, we use an automata learning algorithm to observe the temporal order among destination-related features of network traffic and create a language as a fingerprint. We label fingerprints through machine learning classifiers. We propose our approach in a framework called ML-NetLang for fingerprinting mobile applications from encrypted network traffic. Our evaluation achieves an average accuracy of 95% for Android and iOS applications. ML-NetLang outperforms comparable state-of-the-art techniques using behavioral-based, correlation-based, and machine-learning solutions.

Original languageEnglish
Title of host publication2023 IFIP Networking Conference, IFIP Networking 2023
Place of PublicationPiscataway, NJ
PublisherIEEE
Number of pages9
ISBN (Electronic)978-3-903176-57-7
ISBN (Print)979-8-3503-3938-3
DOIs
Publication statusPublished - 2023
Event22nd International Federation for Information Processing Conference on Networking, IFIP Networking 2023 - Barcelona, Spain
Duration: 12 Jun 202315 Jun 2023

Publication series

NameProceedings IFIP Networking Conference (IFIP Networking)
PublisherIEEE
Volume2023
ISSN (Print)1861-2288

Conference

Conference22nd International Federation for Information Processing Conference on Networking, IFIP Networking 2023
Country/TerritorySpain
CityBarcelona
Period12/06/2315/06/23

Keywords

  • Automata learning
  • Fingerprinting
  • Machine Learning (ML)
  • Traffic classification
  • 2024 OA procedure

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