Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success

• URL: http://arxiv.org/abs/2502.12930v1
• Date: Tue, 18 Feb 2025 15:12:02 GMT
• Title: Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success
• Authors: Jan Luxemburk, Karel Hynek, Richard Plný, Tomáš Čejka,
• Abstract summary: Encrypted traffic classification methods must adapt to new protocols and extensions. We pretrain an embedding model on a complex task with a large number of classes and then transfer it to five well-known TC datasets. A proposed solution, based on nearest neighbors search in the embedding space, surpasses SOTA performance on four of the five TC datasets.
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• 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.

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