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Conference Paper A Study on Distance Measure for Effective Anomaly Detection using AutoEncoder
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Authors
Lee Hyun Yong, Kim Nack Woo, Lee Jungi, Lee Byung-Tak
Issue Date
202010
Source
International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1348-1352
DOI
https://dx.doi.org/10.1109/ICTC49870.2020.9289177
Project Code
20PK1100, Development and Trial of New Business Model and Service using Electric-Power Big-data, Lee Byung-Tak
Abstract
Anomaly detection is a popular application in various areas. One challenging issue is to build an anomaly detection model using normal data because collecting potential abnormal data is quite difficult. In this paper, we build an anomaly detection model using just normal data based on adversarial autoencoder for acoustic data. After extracting features using the trained model, we apply a distance-based method for calculating a threshold to be used for anomaly detection. In particular, we propose a method for reflecting differences in dimensions in calculating distance. Through experiments, we show that the proposed dimension-aware distance measure improves anomaly detection accuracy by up to 7% compared to existing distance measure methods.
KSP Keywords
Acoustic data, Detection accuracy, Detection model, Distance-based, abnormal data, anomaly detection, distance measure