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학술대회 프로시딩

홈 홈 > 연구문헌 > 학술대회 프로시딩 > 한국정보처리학회 학술대회 > 2018년 춘계 학술대회

2018년 춘계 학술대회

Current Result Document : 7,671 / 7,671

한글제목(Korean Title) IoT 네트워크에서 다중 스케일 PCA를 사용한 트렌드 적응형 이상 탐지
영문제목(English Title) Trend-adaptive Anomaly Detection with Multi-Scale PCA in IoT Networks
저자(Author) Thien-Binh Dang   Manh-Hung Tran   Duc-Tai Le   Hyunseung Choo                    
원문수록처(Citation) VOL 25 NO. 01 PP. 0562 ~ 0565 (2018. 05)
한글내용
(Korean Abstract)
영문내용
(English Abstract)
A wide range of IoT applications use information collected from networks of sensors for monitoring and controlling purposes. However, the frequent appearance of fault data makes it difficult to extract correct information, thereby sending incorrect commands to actuators that can threaten human privacy and safety. For this reason, it is necessary to have a mechanism to detect fault data collected from sensors. In this paper, we present a trend-adaptive multi-scale principal component analysis (Trend-adaptive MS-PCA) model for data fault detection. The proposed model inherits advantages of Discrete Wavelet Transform (DWT) in capturing time-frequency information and advantages of PCA in extracting correlation among sensors' data. Experimental results on a real dataset show the high effectiveness of the proposed model in data fault detection.
키워드(Keyword)                              
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