TY - JOUR
T1 - Immune-inspired adaptable error detection for automated teller machines
AU - de Lemos, Rogério
AU - Timmis, Jon
AU - Ayara, Modupe
AU - Forrest, Simon
N1 - Funding Information:
Manuscript received April 26, 2005; revised June 8, 2006. This work was supported by the NCR Financial Solutions Group. This paper was recommended by Associate Editor L. Meng. R. de Lemos is with the University of Kent, Canterbury, Kent CT2 7NF, U.K. (e-mail: [email protected]). J. Timmis is with the University of York, Heslington, York YO10 5DD, U.K. (e-mail: [email protected]). M. Ayara is with the BNP Paribas, London NW1 6AA, U.K. (e-mail: [email protected]). S. Forrest is with the Advanced Technology and Research (AT&R), NCR Financial Solutions Group, Dundee DD2 4SW, U.K. (e-mail: simon.forrest@ scotland.ncr.com). Digital Object Identifier 10.1109/TSMCC.2007.900662
PY - 2007/9
Y1 - 2007/9
N2 - 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.
AB - 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.
KW - Adaptable error detection (AED)
KW - Artificial immune systems (AIS)
KW - Automated teller machines (ATMs)
KW - Availability
KW - Fault tolerance
UR - http://www.scopus.com/inward/record.url?scp=34548290911&partnerID=8YFLogxK
U2 - 10.1109/TSMCC.2007.900662
DO - 10.1109/TSMCC.2007.900662
M3 - Article
AN - SCOPUS:34548290911
SN - 1094-6977
VL - 37
SP - 873
EP - 886
JO - IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
JF - IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
IS - 5
ER -
