Efficient Anomaly Detection Through Confidence Interval Estimation Based on Time Series Analysis 


Vol. 39,  No. 8, pp. 708-715, Aug.  2014


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  Abstract

In this paper, we suggest a method of realtime confidence interval estimation to detect abnormal states of sensor data. For realtime confidence interval estimation, the mean square errors of the exponential smoothing method and moving average method, two of the time series analysis method, where compared, and the moving average method with less errors was applied. When the sensor data passes the bounds of the confidence interval estimation, the administrator is notified through alarming. As the suggested method is for realtime anomaly detection in a ship, an Android terminal was adopted for better communication between the wireless sensor network and users. For safe navigation, an administrator can make decisions promptly and accurately upon emergency situation in a ship by referring to the anomaly detection information through realtime confidence interval estimation.

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  Cite this article

[IEEE Style]

Y. Kim, Y. Heo, J. Park, M. Jeong, "Efficient Anomaly Detection Through Confidence Interval Estimation Based on Time Series Analysis," The Journal of Korean Institute of Communications and Information Sciences, vol. 39, no. 8, pp. 708-715, 2014. DOI: .

[ACM Style]

Yeong-Ju Kim, You-Kyung Heo, Jin-gwan Park, and Min-A Jeong. 2014. Efficient Anomaly Detection Through Confidence Interval Estimation Based on Time Series Analysis. The Journal of Korean Institute of Communications and Information Sciences, 39, 8, (2014), 708-715. DOI: .

[KICS Style]

Yeong-Ju Kim, You-Kyung Heo, Jin-gwan Park, Min-A Jeong, "Efficient Anomaly Detection Through Confidence Interval Estimation Based on Time Series Analysis," The Journal of Korean Institute of Communications and Information Sciences, vol. 39, no. 8, pp. 708-715, 8. 2014.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880