Imperial College London
Alternate

DrMarinaEvangelou

Faculty of Natural Sciences, Department of Mathematics

Senior Lecturer in Statistics
 
 
 
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Contact

 

+44 (0)20 7594 7184m.evangelou

 
 
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Location

 

546Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Gibberd:2017:10.1109/icdmw.2016.0048,
author = {Gibberd, AJ and Evangelou, M and Nelson, JDB},
doi = {10.1109/icdmw.2016.0048},
publisher = {IEEE},
title = {The time-varying dependency patterns of NetFlow statistics},
url = {http://dx.doi.org/10.1109/icdmw.2016.0048},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - We investigate where and how key dependency structure between measures of network activity change throughout the course of daily activity. Our approach to data-mining is probabilistic in nature, we formulate the identification of dependency patterns as a regularised statistical estimation problem. The resulting model can be interpreted as a set of time-varying graphs and provides a useful visual interpretation of network activity. We believe this is the first application of dynamic graphical modelling to network traffic of this kind. Investigations are performed on 9 days of real-world network traffic across a subset of IP's. We demonstrate that dependency between features may change across time and discuss how these change at an intra and inter-day level. Such variation in feature dependency may have important consequences for the design and implementation of probabilistic intrusion detection systems.
AU  - Gibberd,AJ
AU  - Evangelou,M
AU  - Nelson,JDB
DO  - 10.1109/icdmw.2016.0048
PB  - IEEE
PY  - 2017///
TI  - The time-varying dependency patterns of NetFlow statistics
UR  - http://dx.doi.org/10.1109/icdmw.2016.0048
UR  - http://hdl.handle.net/10044/1/42517
ER  -
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