Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.comnet.2017.09.015 | Computer Networks |
Keywords | Field | DocType |
Traffic classification,Flow relationship,Packet loss,Mice flow | Traffic classification,Computer science,Network packet,Flow (psychology),Internet traffic classification,Computer network,Packet loss,Linear complexity | Journal |
Volume | Issue | ISSN |
129 | P1 | 1389-1286 |
Citations | PageRank | References |
0 | 0.34 | 27 |
Authors | ||
4 | ||