VisQUIC

QUIC, a new and increasingly used transport protocol, addresses and resolves the limitations of TCP by offering improved security, performance, and features such as stream multiplexing and connection migration. These features, however, also present challenges for network operators who need to monitor and analyze web traffic. In this paper, we introduce \textit{VisQUIC}, a labeled image-dataset with configurable parameters of window length, pixel resolution, normalization, and labels. To develop the dataset, we captured QUIC traces from more than $9,000$ websites and more than $72,000$ traces over a four-month period. The captured traces are converted into learnable, customizable RGB images, enabling an observer looking at the interactions between a client and a server to analyze and gain insights about QUIC encrypted connections. To illustrate the dataset's potential, we offer a use-case example of an observer estimating the number of HTTP/3 responses/requests pairs in a given QUIC, which can reveal server behavior, client--server interactions, and the load imposed by an observed connection. We formulate the problem as a discrete regression problem, train a machine learning (ML) model for it, and then evaluate it using the proposed dataset. Our use-case example is only one demonstration of the dataset’s application; a number of such uses exist.

QUIC是一种新兴且应用日益广泛的传输协议，它通过提供更优的安全性、性能，以及流多路复用、连接迁移等特性，解决了传输控制协议（TCP）的局限性。然而这些特性也给需要监控与分析Web流量的网络运营商带来了挑战。本文介绍了VisQUIC——一款带有可配置参数（窗口长度、像素分辨率、归一化方式及标签类型）的带标注图像数据集。为构建该数据集，研究团队在四个月的周期内，从超过9000个网站捕获了共计72000余条QUIC流量轨迹。所捕获的流量轨迹被转换为可学习、可自定义的RGB图像，使得研究者通过观察客户端与服务器的交互行为，能够分析并获取QUIC加密连接的相关洞察。为展示该数据集的应用潜力，本文提供了一个用例示例：通过该数据集可估算给定QUIC连接中的HTTP/3响应与请求对数量，该指标可揭示服务器行为、客户端-服务器交互模式，以及观测连接所带来的负载情况。本文将该任务建模为离散回归问题，为此训练了机器学习（ML）模型，并基于所提出的数据集完成了模型评估。本用例仅为该数据集应用的一个演示案例，该数据集尚有诸多其他应用场景。