McGregor, A. J., Hall, M. A., Lorier, P., & Brunskill, J. (2004). Flow clustering using machine learning techniques. In C. Barakat & I. Pratt (Eds.), Passive and Active Network Measurement. PAM 2004. Lecture Notes in Computer Science (Vol. 3015, pp. 205–214). Springer, Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-540-24668-8_21
Permanent Research Commons link: http://hdl.handle.net/10289/10848
Packet header traces are widely used in network analysis. Header traces are the aggregate of traffic from many concurrent applications. We present a methodology, based on machine learning, that can break the trace down into clusters of traffic where each cluster has different traffic characteristics. Typical clusters include bulk transfer, single and multiple transactions and interactive traffic, amongst others. The paper includes a description of the methodology, a visualisation of the attribute statistics that aids in recognising cluster types and a discussion of the stability and effectiveness of the methodology.
This is an author’s accepted version of an article published in Passive and Active Network Measurement. PAM 2004. Lecture Notes in Computer Science, LNCS 3015. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-24668-8_21. © Springer, Berlin, Heidelberg 2004