(Adams, Anagnostopoulos, Hand, Heard,  Montana)

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. This research group is interested in developing statistical methodology for analysing the evolution of such networks, with a strong focus on computationally scalable methods. A principle aim is enabling on-line anomaly detection, so that any intruders or abnormalities in the network can be quickly identified in real time.

The group has active collaborations within Imperial and with external partners both in the UK and the US.

Selected Publications (in chronological order):

Invited Talks / Keynote Presentations:

  • Heard N. (2013) "Monitoring a device in a communication network", RSS Applied Probability section and HIMR one-day workshop on Networks, Bristol, UK
  • Heard N. (2011) "Bayesian Anomaly Detection Methods for Social Networks", Los Alamos National Laboratory, NM, USA
  • Heard N. (2011) "Streaming change point detection methods", Los Alamos National Laboratory, NM, USA
  • Heard N. (2011) "Real Time Anomaly Detection with Applications in Dynamic Networks", Hierarchical Models and Markov Chain Monte Carlo, Hersonissos, Crete


  • "Statistical Aspects of Cyber Security", University of Bristol, March 2013

Impact / Industrial Collaborations / Consultancy:

  • BAE consultancy (2010, 2011)
  • Collaboration with Los Alamos National Laboratory (including an extended PhD internship)

Related Grants:

  • DIF DTC Grant (2009) "Data Mining Tools for Detecting Anomalous Clusters in Network Communications" (Heard) 



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