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{Rubin-Delanchy:2016:10.1109/ISI.2016.7745482,
author = {Rubin-Delanchy, P and HEARD, NA},
doi = {10.1109/ISI.2016.7745482},
publisher = {IEEE},
title = {Disassortativity of computer networks},
url = {http://dx.doi.org/10.1109/ISI.2016.7745482},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Network data is ubiquitous in cyber-security applications. Accurately modelling such data allows discovery of anomalous edges, subgraphs or paths, and is key to many signature-free cyber-security analytics. We present a recurring property of graphs originating from cyber-security applications, often considered a ‘corner case’ in the main literature on network data analysis, that greatly affects the performance of standard ‘off-the-shelf’ techniques. This is the property that similarity, in terms of network behaviour, does not imply connectivity, and in fact the reverse is often true. We call this disassortivity. The phenomenon is illustrated using network flow data collected on an enterprise network, and we show how Big Data analytics designed to detect unusual connectivity patterns can be improved.
AU  - Rubin-Delanchy,P
AU  - HEARD,NA
DO  - 10.1109/ISI.2016.7745482
PB  - IEEE
PY  - 2016///
TI  - Disassortativity of computer networks
UR  - http://dx.doi.org/10.1109/ISI.2016.7745482
UR  - http://hdl.handle.net/10044/1/42764
ER  -
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