Online Statistical Monitoring and Anomaly Detection in Large Networks
Ph.D. Studentship, Dept. of Mathematics and the Institute for Security Science and Technology

Applications are invited for a statistics PhD studentship to commence in October 2009 or as soon as possible thereafter. The studentship is funded by the EPSRC through the new Institute for Security Science and Technology of Imperial College, although the student will be based in the Department of Mathematics. To be eligible for funding, applicants must be UK citizens, or EU citizens resident in the UK for the last three years.

There are many security contexts in which there is a requirement to monitor the evolution of a large, dynamic network. Such networks might be telecommunications between individuals within communities of interest, or data traffic flows on computer networks holding sensitive information. If timely interventions are to be made based on surveillance of the network, then we require computationally scalable methods to enable efficient real-time anomaly detection. Additionally these detection methods should provide insight into the hierarchies within the network and their influences on the anomalous behaviour. At present, such methods are not well developed.

This project seeks to build on earlier work on Bayesian anomaly detection methods for dynamic networks in discrete time, now moving the work forward into the continuous time domain to make use of the precise times at which links in the network strengthen or weaken. In particular, working within this richer framework would open up the possibility of looking at the causal effects of behaviours in the network, so for example inferring whether individual X recently contacting individual Y caused individual Y to contact individual Z.

Besides the publicly available real and simulated data, we now have access to a vast supply of anonymised computer network traffic data. This data resource has a very large number of nodes, approximately 50,000, and real life seasonality issues to overcome; additionally there are very clear targets for anomaly detection which we wish to capture, such as a virus attacking a computer and changing its connection behaviour.

The project is to be supervised by Dr Nick Heard and Professor David Hand. Further project specific information can be obtained from Dr Heard ([log in to unmask]<mailto:[log in to unmask]>). Applicants are expected to have at least a 2.1 degree in a mathematical discipline containing a significant amount of statistics. The ideal candidate would be familiar with advanced statistical methods and possess strong computer programming skills.

This studentship is being re-advertised. The aim is for the successful applicant to start as soon as possible in October 2009, but at the latest no applications will be considered after 30 October 2009.

To apply please email your CV, two references and a personal statement to [log in to unmask]<mailto:[log in to unmask]> or send them by post to Rusudan Svanidze, Department of Mathematics, South Kensington Campus, Imperial College London, SW7 2AZ. Short listed candidates will be asked to complete a postgraduate application form.