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Assessment of Effectiveness of Counterfeit Transaction Detection Systems for Smart Card Based Electronic Cash

  • Kazuo J. Ezawa
  • Gregory Napiorkowski
  • Mariusz Kossarski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1648)

Abstract

In this paper, we discuss a process to evaluate the effectiveness of counterfeit detection systems for an electronic cash scheme which is not fully accounted (i.e., off line, peer to peer transactions are allowed, and no shadow accounting for each purse). The process includes a use of a micro dynamic simulator to simulate various counterfeit scenarios (in addition to testing on the actual non-counterfeit transaction data sets from the real deployment) and generate transaction data sets for detection systems to use for the counterfeit detection systems training and testing. A case study of preliminary test results related to the effectiveness of the detection systems in a simulated counterfeit scenario is also provided.

Keywords

Risk Management Smart Card Counterfeit Attack Counterfeit Detection Risk Management System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Ezawa, K.J., and Napiorkowski, G., 1998, “Assessment of Threats for Smart Card based Electronic Cash,” Financial Cryptography’ 98.Google Scholar
  2. 2.
    Ezawa, K.J. and Schuermann, T., 1995, “Fraud/Uncollectible Debt Detection Using a Bayesian Network Based Learning System: A Rare Binary Outcome with Mixed Data Structures,” Proeedings of the 11th Conference Uncertainty in Artificial Intelligence, Morgan Kaufmann, pp. 157–166.Google Scholar
  3. 3.
    Ezawa, K.J., Singh, M., and Norton, S.W., 1996, “Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management”, Proceedings of the 13th International Conference on Machine Learning, Morgan Kaufmann.Google Scholar
  4. 4.
    Ezawa, K.J., and Norton S., 1996, “Constructing Bayesian Networks to Predict Uncollectible Telecommunications Accounts,” IEEE EXPERT, Vol. 11, No. 5, pp.45–51.CrossRefGoogle Scholar
  5. 5.
    Maher, D.P., 1997, “Fault Induction Attacks, Tamper Resistance, and Hostile Reverse Engineering in Perspective,” Financial Cryptography’ 97-First International Conference, Springer Verlag.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Kazuo J. Ezawa
    • 1
  • Gregory Napiorkowski
    • 1
  • Mariusz Kossarski
    • 1
  1. 1.Mondex International Limited, Atlantic Technology CenterNew JerseyUSA

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