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Sequential Adversarial Anomaly Detection for One-Class Event Data

Author

Listed:
  • Shixiang Zhu

    (Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • Henry Shaowu Yuchi

    (H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

  • Minghe Zhang

    (H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

  • Yao Xie

    (H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

Abstract

We consider the sequential anomaly detection problem in the one-class setting when only the anomalous sequences are available and propose an adversarial sequential detector by solving a minimax problem to find an optimal detector against the worst-case sequences from a generator. The generator captures the dependence in sequential events using the marked point process model. The detector sequentially evaluates the likelihood of a test sequence and compares it with a time-varying threshold, also learned from data through the minimax problem. We demonstrate our proposed method’s good performance using numerical experiments on simulations and proprietary large-scale credit card fraud data sets. The proposed method can generally apply to detecting anomalous sequences.

Suggested Citation

  • Shixiang Zhu & Henry Shaowu Yuchi & Minghe Zhang & Yao Xie, 2023. "Sequential Adversarial Anomaly Detection for One-Class Event Data," INFORMS Joural on Data Science, INFORMS, vol. 2(1), pages 45-59, April.
    • Handle: RePEc:inm:orijds:v:2:y:2023:i:1:p:45-59
    DOI: 10.1287/ijds.2023.0026
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