Internet traffic classification based on expanding vector of flow

  • Lei Ding
  • , Jun Liu
  • , Tao Qin
  • , Haifei Li

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

To reduce the number of packets used in categorizing flows, we propose a new traffic classification method by investigating the relationships between flows instead of considering them individually. Based on the flow identities, we introduce seven types of relationships for a flow and a further Expanding Vector (EV) by searching relevant flows in a particular time window. The proposed Traffic Classification method based on Expanding Vector (TCEV) does not require an inspection of the detailed flow properties, and thus, it can be conducted with a linear complexity of the flow number. The experiments performed on real-world traffic data verify that our method (1) attains as high a performance as the representative methods, while significantly reducing the number of processed packets; (2) is robust against packet loss and the absence of flow direction; and (3) is capable of reaching higher accuracy in the recognition of TCP mice flows.

Original languageEnglish
Pages (from-to)178-192
Number of pages15
JournalComputer Networks
Volume129
DOIs
StatePublished - 24 Dec 2017

Keywords

  • Flow relationship
  • Mice flow
  • Packet loss
  • Traffic classification

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