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arXiv · 2410.06140

Estimating the Number of HTTP/3 Responses in QUIC Using Deep Learning

Barak Gahtan·Robert J. Shahla·Reuven Cohen·Alex M. Bronstein
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Abstract

QUIC, a new and increasingly used transport protocol, enhances TCP by offering improved security, performance, and stream multiplexing. These features, however, also impose challenges for network middle-boxes that need to monitor and analyze web traffic. This paper proposes a novel method to estimate the number of HTTP/3 responses in a given QUIC connection by an observer. This estimation reveals server behavior, client-server interactions, and data transmission efficiency, which is crucial for various applications such as designing a load balancing solution and detecting HTTP/3 flood attacks. The proposed scheme transforms QUIC connection traces into image sequences and uses machine learning (ML) models, guided by a tailored loss function, to predict response counts. Evaluations on more than seven million images-derived from 100,000 traces collected across 44,000 websites over four months-achieve up to 97% accuracy in both known and unknown server settings and 92% accuracy on previously unseen complete QUIC traces.

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Barak Gahtan, Robert J. Shahla, Reuven Cohen, Alex M. Bronstein. 2024-10-08. Estimating the Number of HTTP/3 Responses in QUIC Using Deep Learning. https://arxiv.org/abs/2410.06140

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