{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/universal-embedding-function-for-traffic","title":"Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success","arxiv_id":"2502.12930","date":"2025-02-18","proceeding":null,"authors":["Jan Luxemburk","Karel Hynek","Richard Plný","Tomáš Čejka"],"abstract":"Encrypted traffic classification (TC) methods must adapt to new protocols and extensions as well as to advancements in other machine learning fields. In this paper, we follow a transfer learning setup best known from computer vision. We first pretrain an embedding model on a complex task with a large number of classes and then transfer it to five well-known TC datasets. The pretraining task is recognition of SNI domains in encrypted QUIC traffic, which in itself is a problem for network monitoring due to the growing adoption of TLS Encrypted Client Hello. Our training pipeline -- featuring a disjoint class setup, ArcFace loss function, and a modern deep learning architecture -- aims to produce universal embeddings applicable across tasks. The proposed solution, based on nearest neighbors search in the embedding space, surpasses SOTA performance on four of the five TC datasets. A comparison with a baseline method utilizing raw packet sequences revealed unexpected findings with potential implications for the broader TC field. We published the model architecture, trained weights, and transfer learning experiments.","url_abs":"https://arxiv.org/abs/2502.12930v1","url_pdf":"https://arxiv.org/pdf/2502.12930v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"universal-embedding-function-for-traffic","repo_url":"https://github.com/CESNET/cesnet-models","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"traffic-classification","task_name":"Traffic Classification"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[{"method_slug":"arcface","method_name":"ArcFace"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}