GRU-Based Learning for the Identification of Congestion Protocols in TCP Traffic

2025-09-16

AI Summary

This study addresses the fine-grained identification of TCP congestion control protocols (Reno, CUBIC, Vegas, BBR) in campus networks. We propose a time-series modeling approach based on Gated Recurrent Units (GRUs), distinguishing itself from conventional CNN- or LSTM-based architectures through its lightweight design—preserving temporal modeling capability while significantly improving inference efficiency for highly dynamic, interference-prone real-world campus networks. Evaluated on real-world, flow-level TCP features—including RTT evolution, congestion window dynamics, and packet-loss patterns—the method achieves 97.04% protocol classification accuracy on unseen traffic, substantially outperforming existing statistical-feature-based and shallow-model approaches. Our key contributions are: (i) empirical validation of lightweight RNNs for protocol identification, demonstrating both efficacy and practical deployability; and (ii) provision of a production-ready deep learning baseline for network measurement and adaptive QoS control.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Security and Privacy: Large-scale security measurements

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.

Problem

• Identifying TCP congestion control protocols accurately
• Using GRU-based learning for network traffic analysis
• Achieving high accuracy on complex competitive networks

Innovation

• GRU-based learning model for protocol identification
• Faster neural network architecture on complex network
• Achieved 97.04% accuracy for TCP congestion protocols