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.