Sequential Adversarial Anomaly Detection for One-Class Event Data

2019/10/21 by Shixiang Zhu, Zhu, Shixiang, Henry Shaowu Yuchi +5 · 2 citations
Computer Science · Decision Sciences · Engineering · #Advanced Statistical Process Monitoring #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Fault Detection and Control Systems #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.1910.09161

openalex publication_date 2019/10/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• On distinguishability criteria for estimating generative models
• A Recurrent Latent Variable Model for Sequential Data
• CONTINUOUS INSPECTION SCHEMES
• Learning Temporal Point Processes via Reinforcement Learning
• Deep Fourier Kernel for Self-Attentive Point Processes

Cited by

• Countering adversarial evasion in regression analysis
• Adversarial training with restricted data manipulation

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