Proceedings of the Korea Information Processing Society Conference (한국정보처리학회:학술대회논문집)
- 2019.10a
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- Pages.145-148
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- 2019
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- 2005-0011(pISSN)
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- 2671-7298(eISSN)
DOI QR Code
Joint Exponential Smoothing and Trend-based Principal Component Analysis for Anomaly Detection in Wireless Sensor Networks
무선 센서 네트워크에서의 이상 징후 감지를 위한 공동 지수 평활법 및 추세 기반 주성분 분석
- Dang, Thien-Binh (College of Software, Sungkyunkwan University) ;
- Yang, Hui-Gyu (College of Software, Sungkyunkwan University) ;
- Tran, Manh-Hung (College of Software, Sungkyunkwan University) ;
- Le, Duc-Tai (College of Software, Sungkyunkwan University) ;
- Kim, Moonseong (Dept. of Liberal Arts, Seoul Theological University) ;
- Choo, Hyunseung (College of Software, Sungkyunkwan University)
- Published : 2019.10.30
Abstract
Principal Component Analysis (PCA) is a powerful technique in data analysis and widely used to detect anomalies in Wireless Sensor Networks. However, the performance of conventional PCA is not high on time-series data collected by sensors. In this paper, we propose a Joint Exponential Smoothing and Trend-based Principal Component Analysis (JES-TBPCA) for Anomaly Detection which is based on conventional PCA. Experimental results on a real dataset show a remarkably higher performance of JES-TBPCA comparing to conventional PCA model in detection of stuck-at and offset anomalies.
Keywords