Imperial College London
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ProfessorNiallAdams

Faculty of Natural Sciences, Department of Mathematics

Professor of Statistics
 
 
 
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Contact

 

+44 (0)20 7594 8837n.adams Website

 
 
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Location

 

6M55Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Schon:2017:10.1109/ISI.2017.8004870,
author = {Schon, C and Adams, NM and Evangelou, M},
doi = {10.1109/ISI.2017.8004870},
pages = {31--36},
publisher = {IEEE},
title = {Clustering and monitoring edge behaviour in enterprise network traffic},
url = {http://dx.doi.org/10.1109/ISI.2017.8004870},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - This paper takes an unsupervised learning approach for monitoring edge activity within an enterprise computer network. Using NetFlow records, features are gathered across the active connections (edges) in 15-minute time windows. Then, edges are grouped into clusters using the k-means algorithm. This process is repeated over contiguous windows. A series of informative indicators are derived by examining the relationship of edges with the observed cluster structure. This leads to an intuitive method for monitoring network behaviour and a temporal description of edge behaviour at global and local levels.
AU  - Schon,C
AU  - Adams,NM
AU  - Evangelou,M
DO  - 10.1109/ISI.2017.8004870
EP  - 36
PB  - IEEE
PY  - 2017///
SP  - 31
TI  - Clustering and monitoring edge behaviour in enterprise network traffic
UR  - http://dx.doi.org/10.1109/ISI.2017.8004870
UR  - http://hdl.handle.net/10044/1/48810
ER  -
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