Seiðr: Dataplane Assisted Flow Classification Using ML

Simpson, K. A. , Cziva, R. and Pezaros, D. P. (2021) Seiðr: Dataplane Assisted Flow Classification Using ML. In: IEEE GLOBECOM 2020, Taipei, Taiwan, 07-11 Dec 2020, ISBN 9781728182988 (doi: 10.1109/GLOBECOM42002.2020.9348063)

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222492.pdf - Accepted Version



Real-time, high-speed flow classification is fundamental for network operation tasks, including reactive and proactive traffic engineering, anomaly detection and security enhancement. Existing flow classification solutions, however, do not allow operators to classify traffic based on fine-grained, temporal dynamics due to imprecise timing, often rely on sampled data, or only work with low traffic volumes and rates. In this paper, we present Seiðr, a classification solution that: (i) uses precision timing, (ii) has the ability to examine every packet on the network, (iii) classifies very high traffic volumes with high precision. To achieve this, Seiðr exploits the data aggregation and timestamping functionality of programmable dataplanes. As a concrete example, we present how Seiðr can be used together with Machine Learning algorithms (such as CNN, k -NN) to provide accurate, real-time and high-speed TCP congestion control classification, separating TCP BBR from its predecessors with over 88–96% accuracy and F1-score of 0.864-0.965, while only using 15.5 MiB of memory in the dataplane.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Cziva, Mr Richard and Simpson, Dr Kyle and Pezaros, Professor Dimitrios
Authors: Simpson, K. A., Cziva, R., and Pezaros, D. P.
College/School:College of Science and Engineering > School of Computing Science
Copyright Holders:Copyright © 2020 IEEE
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
172865EPSRC DTP 16/17 and 17/18Tania GalabovaEngineering and Physical Sciences Research Council (EPSRC)EP/N509668/1Research and Innovation Services
172888Network Measurement as a Service (MaaS)Dimitrios PezarosEngineering and Physical Sciences Research Council (EPSRC)EP/N033957/1Computing Science