Estimating the Number of HTTP/3 Responses in QUIC Using Deep Learning
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910919852818432 |
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| author | Gahtan, Barak Shahla, Robert J. Cohen, Reuven Bronstein, Alex M. |
| author_facet | Gahtan, Barak Shahla, Robert J. Cohen, Reuven Bronstein, Alex M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_06140 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Estimating the Number of HTTP/3 Responses in QUIC Using Deep Learning Gahtan, Barak Shahla, Robert J. Cohen, Reuven Bronstein, Alex M. Machine Learning Computer Vision and Pattern Recognition Networking and Internet Architecture 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. |
| title | Estimating the Number of HTTP/3 Responses in QUIC Using Deep Learning |
| topic | Machine Learning Computer Vision and Pattern Recognition Networking and Internet Architecture |
| url | https://arxiv.org/abs/2410.06140 |