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Details of Grant 

EPSRC Reference: GR/R55184/01
Title: Data mining Tools for Fraud Detection in M-Commerce - DETECTOR
Principal Investigator: Girolami, Professor M
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Memex Technology Ltd NTL Ltd
Department: Computing Science
Organisation: University of the West of Scotland
Scheme: LINK
Starts: 20 February 2002 Ends: 31 March 2004 Value (£): 246,922
EPSRC Research Topic Classifications:
Mobile Computing Modelling & simul. of IT sys.
Networks & Distributed Systems
EPSRC Industrial Sector Classifications:
Financial Services Communications
Retail
Related Grants:
Panel History:  
Summary on Grant Application Form
The effective detection and subsequent analysis of the types of fraudulent activity which occur within telecommunications systems is varied and changes with the emergence of new technologies and new forms of commercial activity (e&m-commerce). The dynamic nature of fraudulent activity as well as the dynamic and changing nature of normal usage of a service has rendered the detection of fraudulent intent from observed behavioural patterns a research problem of some considerable difficulty. It is proposed that a common probabilistic theoretical framework be employed in the development of dynamic behavioural models which combine a number of prototypical behavioural aspects to define a model of acceptable behaviour (e.g. usage of a mobile phone, web-browsing patterns) from which inferences of the probability of abnormal behaviour can be made. In addition to these inferential models a means of visualising the observed behaviour and the intentions behind it (based on call records, web activity, or purchasing patterns) will significanity aid the pattern recognition abilities of human fraud analysts. Employing the common probabilistic modeling framework which defines the 'fraud detection' models visualisation tools will be developed to provide meaningful visual representations of dynamic activity which has been observed and visualisations of the evolution of the underlying states (or user intentions) generating the observed activity. The development of detection & analysis tools from the common theoretical framework will provide enhanced detection and analysis capability in the identification of fraud.
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Organisation Website: http://www.uws.ac.uk