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

By Thijs Van Ede, Riccardo Bortolameotti, Andrea Continella, Jingjing Ren, Daniel J. Dubois, Martina Lindorfer, David R. Choffnes, Maarten van Steen, Andreas Peter. Network and Distributed System Security Symposium, 2020

367 citations recorded by the source corpus.

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. As a solution, we propose FlowPrint, a semi-supervised approach for fingerprinting mobile apps from (encrypted) network traffic. We automatically find temporal correlations among destination-related features of network traffic and use these correlations to generate app fingerprints. Our approach is able to fingerprint previously unseen apps, something that existing techniques fail to achieve. We evaluate our approach for both Android and iOS in the setting of app recognition, where we achieve an accuracy of 89.2%, significantly outperforming state-of-the-art solutions. In addition, we show that our approach can detect previously unseen apps with a precision of 93.5%, detecting 72.3% of apps within the first five minutes of communication.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/f9296d55d45c2ce271155f5aa818058fc24dea0b

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In Artificial Intelligence

1. Gang Xiong
2. Zhen Li
3. Jiangtao Yu

Citations in the corpus

• ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification (2022, methodology): “Early works [35] leverage the remaining plaintext in the encrypted traffic (e.”
• Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities (2024, methodology): “…ET-BERT, the authors conduct experiments across several encrypted traffic classification tasks, e.g., general encrypted application classification [132], encrypted malware classification, encrypted traffic classification on VPN [133], with the remarkable improvements over existing…”