Recurrent Neural Network-Based Prediction of TCP Transmission States from Passive Measurements

Abstract

Long Short-Term Memory (LSTM) neural networks are a state-of-the-art techniques when it comes to sequence learning and time series prediction models. In this paper, we have used LSTM-based Recurrent Neural Networks (RNN) for building a generic prediction model for Transmission Control Protocol (TCP) connection characteristics from passive measurements. To the best of our knowledge, this is the first work that attempts to apply LSTM for demonstrating how a network operator can identify the most important system-wide TCP per-connection states of a TCP client that determine a network condition (e.g., cwnd) from passive traffic measured at an intermediate node of the network without having access to the sender. We found out that LSTM learners outperform the state-of-the-art classical machine learning prediction models. Through an extensive experimental evaluation on multiple scenarios, we demonstrate the scalability and robustness of our approach and its potential for monitoring TCP transmission states related to network congestion from passive measurements. Our results based on emulated and realistic settings suggest that Deep Learning is a promising tool for monitoring system-wide TCP states from passive measurements and we believe that the methodology presented in our paper may strengthen future research work in the computer networking community.