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.
Cite
@article{arxiv.1910.09161,
title = {Sequential Adversarial Anomaly Detection for One-Class Event Data},
author = {Shixiang Zhu and Henry Shaowu Yuchi and Minghe Zhang and Yao Xie},
journal= {arXiv preprint arXiv:1910.09161},
year = {2023}
}