City Research Online

Classification of traffic flows into QoS classes by unsupervised learning and KNN clustering

Chen, T. and Zeng, Y. (2009). Classification of traffic flows into QoS classes by unsupervised learning and KNN clustering. KSII Trans. on Internet and Information Systems, 3(2), pp. 134-146. doi: 10.3837/tiis.2009.02.001

Abstract

Traffic classification seeks to assign packet flows to an appropriate quality of service (QoS) class based on flow statistics without the need to examine packet payloads. Classification proceeds in two steps. Classification rules are first built by analyzing traffic traces, and then the classification rules are evaluated using test data. In this paper, we use self-organizing map and K-means clustering as unsupervised machine learning methods to identify the inherent classes in traffic traces. Three clusters were discovered, corresponding to transactional, bulk data transfer, and interactive applications. The K-nearest neighbor classifier was found to be highly accurate for the traffic data and significantly better compared to a minimum mean distance classifier.

Publication Type: Article
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Departments: School of Mathematics, Computer Science & Engineering > Engineering
School of Mathematics, Computer Science & Engineering > Engineering > Electrical & Electronic Engineering
URI: http://openaccess.city.ac.uk/id/eprint/8212
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