GRU-Based Learning for the Identification of Congestion Protocols in TCP Traffic
Paul Bergeron, Sandhya Aneja
公開日: 2025/9/16
Abstract
This paper presents the identification of congestion control protocols TCP Reno, TCP Cubic, TCP Vegas, and BBR on the Marist University campus, with an accuracy of 97.04% using a GRU-based learning model. We used a faster neural network architecture on a more complex and competitive network in comparison to existing work and achieved comparably high accuracy.