@inproceedings{8d287e70e76846ceabd42442420f8720,
title = "Mobile App Fingerprinting through Automata Learning and Machine Learning",
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.",
keywords = "Automata learning, Fingerprinting, Machine Learning (ML), Traffic classification, 2024 OA procedure",
author = "Fatemeh Marzani and Fatemeh Ghassemi and Zeynab Sabahi-Kaviani and \{van Ede\}, Thijs and \{van Steen\}, Maarten",
note = "Publisher Copyright: {\textcopyright} 2023 IFIP.; 22nd International Federation for Information Processing Conference on Networking, IFIP Networking 2023 ; Conference date: 12-06-2023 Through 15-06-2023",
year = "2023",
doi = "10.23919/IFIPNetworking57963.2023.10186420",
language = "English",
isbn = "979-8-3503-3938-3",
series = "Proceedings IFIP Networking Conference (IFIP Networking)",
publisher = "IEEE",
booktitle = "2023 IFIP Networking Conference, IFIP Networking 2023",
address = "United States",
}