The Department of Computer Science at the University of Cyprus cordially invites you to the Colloquium entitled:
Internet Traffic Classification using Energy Time-Frequency Distributions
Speaker: Dr. Angelos K. Marnerides
We present a fundamentally new approach to classify application flows based on the mapping of aggregate transport-layer volume information onto the Time-Frequency (TF) plane. We initially show that the volume persona (i.e. counts of packets and bytes) of traffic flows at the transport layer exhibits highly non-stationary characteristics, hence rendering many typical classification methods inapplicable. By virtue of this constraint, we present a novel application classification method based on the Cohen energy TF distributions for such highly non-stationary signals. We have used the Rényi information to measure the distinct complexity of any given application signal, and to subsequently construct a robust training model for every application protocol within our scheme. The effectiveness of our approach is demonstrated using real backbone and edge link network traces captured in US and Japan. Our results show that for the majority of applications, aggregate volume-based classification can reach up to 96% accuracy, while considering significantly less features in comparison with existing approaches.
Angelos K. Marnerides obtained his M.Sc and PhD in Computer Science from Lancaster University in 2007 and 2011 respectively. He is currently a postdoctoral research associate in the department of Computing & Communications at Lancaster University and an honorary research associate with the department of Electronic and Electrical Engineering at UCL. Prior to that he was a joint postdoctoral research fellow at the Carnegie Mellon University- University of Porto under the CMU-Portugal postdoctoral scheme. His research interests span in the broad domains of network resilience, network security and network management for next generation networks with a particular interest on traffic characterisation and profiling using statistical signal processing, machine learning and information-theoretic approaches. He is a member of the IEEE and ACM.
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