Sequential Adversarial Anomaly Detection for One-Class Event Data

Shixiang Zhu; Henry Shaowu Yuchi; Minghe Zhang; Yao Xie

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arXiv · 1910.09161

Sequential Adversarial Anomaly Detection for One-Class Event Data

Shixiang Zhu·Henry Shaowu Yuchi·Minghe Zhang·Yao Xie

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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 datasets. The proposed method can generally apply to detecting anomalous sequences.

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BibTeX RIS

Shixiang Zhu, Henry Shaowu Yuchi, Minghe Zhang, Yao Xie. 2019-10-21. Sequential Adversarial Anomaly Detection for One-Class Event Data. https://arxiv.org/abs/1910.09161

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