Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4954573 | Computer Networks | 2017 | 17 Pages |
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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Networks and Communications
Authors
Lei Ding, Jun Liu, Tao Qin, Haifei Li,