Proceedings of the Korea Information Processing Society Conference (한국정보처리학회:학술대회논문집)
- 2023.05a
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- Pages.2-4
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- 2023
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- 2005-0011(pISSN)
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- 2671-7298(eISSN)
DOI QR Code
Sequence Anomaly Detection based on Diffusion Model
확산 모델 기반 시퀀스 이상 탐지
- Zhiyuan Zhang (Dept. of Computer Science, Hanyang University) ;
- Inwhee, Joe (Dept. of Computer Science, Hanyang University)
- Published : 2023.05.18
Abstract
Sequence data plays an important role in the field of intelligence, especially for industrial control, traffic control and other aspects. Finding abnormal parts in sequence data has long been an application field of AI technology. In this paper, we propose an anomaly detection method for sequence data using a diffusion model. The diffusion model has two major advantages: interpretability derived from rigorous mathematical derivation and unrestricted selection of backbone models. This method uses the diffusion model to predict and reconstruct the sequence data, and then detects the abnormal part by comparing with the real data. This paper successfully verifies the feasibility of the diffusion model in the field of anomaly detection. We use the combination of MLP and diffusion model to generate data and compare the generated data with real data to detect anomalous points.
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