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http://dx.doi.org/10.9728/dcs.2018.19.2.377

Design of Anomaly Detection System Based on Big Data in Internet of Things  

Na, Sung Il (Graduate School of Information Security, Korea University)
Kim, Hyoung Joong (Graduate School of Information Security, Korea University)
Publication Information
Journal of Digital Contents Society / v.19, no.2, 2018 , pp. 377-383 More about this Journal
Abstract
Internet of Things (IoT) is producing various data as the smart environment comes. The IoT data collection is used as important data to judge systems's status. Therefore, it is important to monitor the anomaly state of the sensor in real-time and to detect anomaly data. However, it is necessary to convert the IoT data into a normalized data structure for anomaly detection because of the variety of data structures and protocols. Thus, we can expect a good quality effect such as accurate analysis data quality and service quality. In this paper, we propose an anomaly detection system based on big data from collected sensor data. The proposed system is applied to ensure anomaly detection and keep data quality. In addition, we applied the machine learning model of support vector machine using anomaly detection based on time-series data. As a result, machine learning using preprocessed data was able to accurately detect and predict anomaly.
Keywords
Anomaly Detection; Outlier Prediction; Internet of Things; Smart Service; Big Data Analytics;
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Times Cited By KSCI : 4  (Citation Analysis)
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