Sequential Adversarial Anomaly Detection for One-Class Event Data

• URL: http://arxiv.org/abs/1910.09161v5
• Date: Thu, 6 Apr 2023 02:06:52 GMT
• Title: Sequential Adversarial Anomaly Detection for One-Class Event Data
• Authors: Shixiang Zhu, Henry Shaowu Yuchi, Minghe Zhang, Yao Xie
• Abstract summary: We consider the sequential anomaly detection problem in the one-class setting when only the anomalous sequences are available. We propose an adversarial sequential detector by solving a minimax problem to find an optimal detector against the worst-case sequences from a generator.
• Score: 18.577418448786634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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