Sequential Adversarial Anomaly Detection for One-Class Event Data
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionPoint ProcessesMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Graph Neural Networks based Log Anomaly Detection and Explanation
Event logs are widely used to record the status of high-tech systems, making log anomaly detection important for monitoring those systems. Most existing log anomaly detection methods take a log event count matrix or log …
Anomaly DetectionGraph Neural NetworkMulti-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection
Discrete event sequences are ubiquitous, such as an ordered event series of process interactions in Information and Communication Technology systems. Recent years have witnessed increasing efforts in detecting anomalies …
Anomaly DetectionDetecting Log Anomalies with Multi-Head Attention (LAMA)
Anomaly detection is a crucial and challenging subject that has been studied within diverse research areas. In this work, we explore the task of log anomaly detection (especially computer system logs and user behavior lo…
Anomaly DetectionAnomaly Detection via Minimum Likelihood Generative Adversarial Networks
Anomaly detection aims to detect abnormal events by a model of normality. It plays an important role in many domains such as network intrusion detection, criminal activity identity and so on. With the rapidly growing siz…
Anomaly DetectionIntrusion DetectionNetwork Intrusion DetectionA Bi-LSTM Autoencoder Framework for Anomaly Detection -- A Case Study of a Wind Power Dataset
Anomalies refer to data points or events that deviate from normal and homogeneous events, which can include fraudulent activities, network infiltrations, equipment malfunctions, process changes, or other significant but …
Anomaly DetectionTime SeriesTime Series Anomaly Detection