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Perspectives on Adversarial Classification

Author

Listed:
  • David Rios Insua

    (School of Management, University of Shanghai for Science and Technology, Shanghai 201206, China
    ICMAT-CSIC, 28049 Madrid, Spain)

  • Roi Naveiro

    (ICMAT-CSIC, 28049 Madrid, Spain)

  • Victor Gallego

    (ICMAT-CSIC, 28049 Madrid, Spain)

Abstract

Adversarial classification (AC) is a major subfield within the increasingly important domain of adversarial machine learning (AML). So far, most approaches to AC have followed a classical game-theoretic framework. This requires unrealistic common knowledge conditions untenable in the security settings typical of the AML realm. After reviewing such approaches, we present alternative perspectives on AC based on adversarial risk analysis.

Suggested Citation

  • David Rios Insua & Roi Naveiro & Victor Gallego, 2020. "Perspectives on Adversarial Classification," Mathematics, MDPI, vol. 8(11), pages 1-21, November.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:11:p:1957-:d:440180
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    References listed on IDEAS

    as
    1. Jesus Rios & David Rios Insua, 2012. "Adversarial Risk Analysis for Counterterrorism Modeling," Risk Analysis, John Wiley & Sons, vol. 32(5), pages 894-915, May.
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    3. Insua, Insua Rios & Rios, Jesus & Banks, David, 2009. "Adversarial Risk Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 841-854.
    4. Kim, Ji-Hyun, 2009. "Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3735-3745, September.
    5. Ross D. Shachter, 1986. "Evaluating Influence Diagrams," Operations Research, INFORMS, vol. 34(6), pages 871-882, December.
    6. Jason R. W. Merrick & Laura A. McLay, 2010. "Is Screening Cargo Containers for Smuggled Nuclear Threats Worthwhile?," Decision Analysis, INFORMS, vol. 7(2), pages 155-171, June.
    Full references (including those not matched with items on IDEAS)

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