Immune-inspired adaptable error detection for automated teller machines

Rogério de Lemos*, Jon Timmis, Modupe Ayara, Simon Forrest

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

36 Citations (SciVal)


This paper presents an immune-inspired adaptable error detection (AED) framework for automated teller machines (ATMs). This framework has two levels: one is local to a single ATM, while the other is network-wide. The framework employs 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 proposed approach was confirmed in terms of classification performance and impact on availability. The overall results are encouraging as the downtime of ATMs can de reduced by anticipating the occurrence of failures before they actually occur.

Original languageEnglish
Pages (from-to)873-886
Number of pages14
JournalIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Issue number5
Publication statusPublished - Sept 2007


  • Adaptable error detection (AED)
  • Artificial immune systems (AIS)
  • Automated teller machines (ATMs)
  • Availability
  • Fault tolerance


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