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

• URL: http://arxiv.org/abs/2410.06140v1
• Date: Tue, 8 Oct 2024 15:40:22 GMT
• Title: Estimating the Number of HTTP/3 Responses in QUIC Using Deep Learning
• Authors: Barak Gahtan, Robert J. Shahla, Reuven Cohen, Alex M. Bronstein,
• Abstract summary: This paper proposes a novel solution for estimating the number of HTTP/3 responses in a given QUIC connection by an observer. The proposed scheme transforms QUIC connection traces into a sequence of images and trains machine learning (ML) models to predict the number of responses. The scheme achieves up to 97% cumulative accuracy in both known and unknown web server settings and 92% accuracy in estimating the total number of responses in unseen QUIC traces.
• Score: 7.795761092358769
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: QUIC, a new and increasingly used transport protocol, enhances TCP by providing better security, performance, and features like 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 solution for estimating 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 a sequence of images and trains machine learning (ML) models to predict the number of responses. Then, by aggregating images of a QUIC connection, an observer can estimate the total number of responses. As the problem is formulated as a discrete regression problem, we introduce a dedicated loss function. The proposed scheme is evaluated on a dataset of over seven million images, generated from $100,000$ traces collected from over $44,000$ websites over a four-month period, from various vantage points. The scheme achieves up to 97\% cumulative accuracy in both known and unknown web server settings and 92\% accuracy in estimating the total number of responses in unseen QUIC traces.

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