# Sequential Adversarial Anomaly Detection for One-Class Event Data

arXiv: https://arxiv.org/abs/1910.09161 · HTML: https://mlsanity.com/paper/1910.09161/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2019.

## Built on by (most cited first, 9 of 9)

- [Neural Spectral Marked Point Processes](https://mlsanity.com/paper/2106.10773/) (2021)
- [Conditional Generative Modeling for High-dimensional Marked Temporal Point Processes](https://mlsanity.com/paper/2305.12569/) (2023)
- [OIL-AD: An Anomaly Detection Framework for Sequential Decision Sequences](https://mlsanity.com/paper/2402.04567/) (2024)
- [Sequential Change Point Detection via Denoising Score Matching](https://mlsanity.com/paper/2501.12667/) (2025)
- [Recurrent Neural Goodness-of-Fit Test for Time Series](https://mlsanity.com/paper/2410.13986/) (2024)
- [Deep Anomaly Detection and Search via Reinforcement Learning](https://mlsanity.com/paper/2208.14834/) (2022)
- [Unsupervised Event Outlier Detection in Continuous Time](https://mlsanity.com/paper/2411.16427/) (2024)
- [Countering adversarial evasion in regression analysis](https://mlsanity.com/paper/2509.22113/) (2025)
- [Adversarial training with restricted data manipulation](https://mlsanity.com/paper/2510.03254/) (2025)

## Abstract (arXiv)

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.