Immunising automated teller machines

Authors:
Modupe Ayara;Jon Timmis;Rogério de Lemos;Simon Forrest
Affiliations:
Computing Laboratory, University of Kent, Canterbury, UK;Departments of Electronics and Computer Science, University of York, York, UK;Computing Laboratory, University of Kent, Canterbury, UK;NCR Financial Solutions Group, Discovery Centre, Dundee, UK
Venue:
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Year:
2005

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Abstract

This paper presents an immune-inspired adaptable error detection (AED) framework for Automated Teller Machines (ATMs). This framework two levels, one level is local to a single ATM, while the other is a network-wide adaptable error detection. It employs ideas from vaccination, and adaptability analogies of the immune system. For discriminating between normal and erroneous states, an immune inspired one-class supervised algorithm was employed, which supports continual learning and adaptation. The effectiveness of the local AED was confirmed by its ability of detecting potential failures on an average 3 hours before the actual occurrence. This is an encouraging result in terms of availability, since measures can be devised for reducing the downtime of ATMs.