Estimating the Number of HTTP/3 Responses in QUIC Using Deep Learning
<|reference_start|>Estimating the Number of HTTP/3 Responses in QUIC Using Deep Learning: 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.<|reference_end|>
@article{gahtan2024estimating,
    title={Estimating the Number of HTTP/3 Responses in QUIC Using Deep Learning},
    author={Barak Gahtan, Robert J. Shahla, Reuven Cohen, Alex M. Bronstein},
    journal={arXiv preprint arXiv:2410.06140},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06140},
    primaryClass={cs.LG cs.CV cs.NI}
}