用深度学习从QUIC数据流估算HTTP/3响应数，准确率达97%
Estimating the Number of HTTP/3 Responses in QUIC Using Deep Learning
- 将QUIC连接痕迹转为图像序列，用带定制损失函数的机器学习模型预测响应数量
- 在超700万张图像上测试，已知与未知服务器场景下准确率最高达97%
- 适用于负载均衡设计和流量攻击检测，尤其适合网络监控与安全分析人员
QUIC是一种新兴且广泛使用的传输协议，相比TCP提供了更好的安全性和性能，并支持多路复用。然而这些特性也给需要监控和分析网络流量的中间设备带来挑战。本文提出一种新方法，通过观察者估计给定QUIC连接中的HTTP/3响应数量。该估计可揭示服务器行为、客户端-服务器交互及数据传输效率，对负载均衡设计和检测HTTP/3洪水攻击等应用至关重要。该方案将QUIC连接痕迹转化为图像序列，利用机器学习模型并结合定制损失函数进行响应数预测。基于超过七百万张图像（源自10万个连接迹线，覆盖4.4万个网站，持续四个月）的评估，在已知与未知服务器设置下准确率最高达97%，在全新完整QUIC迹线上准确率为92%。
原文摘要 · Abstract (English)
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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