Passive TCP Identification for Wired and WirelessNetworks: A Long-Short Term Memory Approach

TL;DR

This paper introduces a passive TCP identification method using a 4-layer LSTM model that accurately distinguishes TCP algorithms in wired and wireless networks, including new algorithms, with over 98% accuracy.

Contribution

It presents a novel machine learning approach employing LSTM for passive TCP identification applicable to both wired and wireless networks, including emerging algorithms.

Findings

Achieves over 98% accuracy in TCP identification.

LSTM outperforms other machine learning models.

Effective for both wired and wireless networks.

Abstract

Transmission control protocol (TCP) congestion control is one of the key techniques to improve network performance. TCP congestion control algorithm identification (TCP identification) can be used to significantly improve network efficiency. Existing TCP identification methods can only be applied to limited number of TCP congestion control algorithms and focus on wired networks. In this paper, we proposed a machine learning based passive TCP identification method for wired and wireless networks. After comparing among three typical machine learning models, we concluded that the 4-layers Long Short Term Memory (LSTM) model achieves the best identification accuracy. Our approach achieves better than 98% accuracy in wired and wireless networks and works for newly proposed TCP congestion control algorithms.