Directional anomaly detection
Semi-supervised anomaly detection is based on the principle that potential anomalies are those records that look different from normal training data. However, in some cases we are specifically interested in anomalies that correspond to high attribute values (or low, but not both). We present two asymmetrical distance measures that take this directionality into account: ramp distance and signed distance. Through experiments on synthetic and real-life datasets we show that ramp distance performs as well or better than the absolute distance traditionally used in anomaly detection. While signed distance also performs well on synthetic data, it performs substantially poorer on real-life datasets. We argue that this reflects the fact that in practice, good scores on some attributes should not be allowed to compensate for bad scores on others.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionAttributeSemi-supervised Anomaly DetectionSupervised Anomaly DetectionSimilar Papers 제목 키워드 기반
Semi-Supervised Learning for Anomaly Traffic Detection via Bidirectional Normalizing Flows
With the rapid development of the Internet, various types of anomaly traffic are threatening network security. We consider the problem of anomaly network traffic detection and propose a three-stage anomaly detection fram…
Anomaly DetectionBenchmarkingA Multi-directional Meta-Learning Framework for Class-Generalizable Anomaly Detection
In this paper, we address the problem of class-generalizable anomaly detection, where the objective is to develop a unified model by focusing our learning on the available normal data and a small amount of anomaly data i…
Anomaly DetectionDual-encoder Bidirectional Generative Adversarial Networks for Anomaly Detection
Generative adversarial networks (GANs) have shown promise for various problems including anomaly detection. When anomaly detection is performed using GAN models that learn only the features of normal data samples, data t…
Anomaly DetectionSmart Metering System Capable of Anomaly Detection by Bi-directional LSTM Autoencoder
Anomaly detection is concerned with a wide range of applications such as fault detection, system monitoring, and event detection. Identifying anomalies from metering data obtained from smart metering system is a critical…
Anomaly DetectionEvent DetectionFault DetectionAnomaly Detection by Leveraging Incomplete Anomalous Knowledge with Anomaly-Aware Bidirectional GANs
The goal of anomaly detection is to identify anomalous samples from normal ones. In this paper, a small number of anomalies are assumed to be available at the training stage, but they are assumed to be collected only fro…
Anomaly DetectionGenerative Adversarial Network