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Estimation of TCP Congestion Control Algorithms by Deep Recurrent Neural Network

Authors:
Takuya Sawada
Ryo Yamamoto
Satoshi Ohzahata
Toshihiko Kato

Keywords: TCP; Congestion Control; Deep Recurrent Neural Network.

Abstract:
Recently, as various types of networks are introduced, a number of TCP congestion control algorithms have been adopted. Since the TCP congestion control algorithms affect traffic characteristics in the Internet, it is important for network operators to analyze which algorithms are used widely in their backbone networks. In such an analysis, a lot of TCP flows need to be handled and so the automatic processing is indispensable. This paper proposes a machine learning based method for estimating TCP congestion control algorithms. The proposed method uses a passively collected packet traces including both data and ACK segments, and calculates a time sequence of congestion window size for individual TCP flows contained in the traces. We use a classifier based on deep recurrent neural network in the congestion control algorithm estimation. As the results of applying the proposed classifier to ten congestion control algorithms, we obtained high accuracy of classification compared with our previous work using recurrent neural network with one hidden layer.

Pages: 19 to 24

Copyright: Copyright (c) IARIA, 2022

Publication date: November 13, 2022

Published in: conference

ISSN: 2326-9383

ISBN: 978-1-61208-993-5

Location: Valencia, Spain

Dates: from November 13, 2022 to November 17, 2022