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Bayesian Anomaly Detection for Cyber-Security Applications



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Speaker: Patrick Rubin-Delanchy

Abstract: In this talk, I outline a general modus operandi under which to perform intrusion detection at scale. The over-arching principle is this: a network monitoring tool has access to large stores of data on which it can learn `normal' network behaviour. On the other hand, data on intrusions are relatively rare. This imbalance invites us to frame intrusion detection as an anomaly detection problem where, under the null hypothesis that there is no intrusion, the data follow a machine-learnt model of behaviour, and, under the alternative that there is some form of intrusion, certain anomalies in that model will be apparent. This approach to cyber-security poses some important statistical challenges. One is modelling a complex data structure, that is at the same time a set of point processes and a network. Another is the actual deployment of statistical methodology over such large scale and heterogeneous data. Finally, a number of anomaly detection problems arise, for example, `finding a needle in a haystack', combining anomalies through time and across the network, and incorporating model uncertainty.

Short Bio: Patrick Rubin-Delanchy obtained a PhD in Statistics from Imperial College London in 2008. Since November 2015, he has held a Heilbronn Research fellowship at the University of Oxford, in the department of Statistics. His research focuses on Bayesian modelling of complex data structures, particularly point processes, networks, and Big Data, with applications in cyber-security, biophysics, and more.

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When and where

2.30pm - 4.30pmThursday 12th May 2016

ELG08 Drysdale Building City, University of London Northampton Square London EC1V 0HB United Kingdom

Contact Details

Dr Vladimir Stankovic


Centre for Software Reliability

School of Mathematics, Computer Science & Engineering


College Building
City, University of London
Northampton Square
United Kingdom
+44 (0)20 7040 3079

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