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Data Fusion Algorithm based on Inference for Anomaly Detection in the Next-Generation Intrusion Detection
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 Title & Authors
Data Fusion Algorithm based on Inference for Anomaly Detection in the Next-Generation Intrusion Detection
Kim, Dong-Wook; Han, Myung-Mook;
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In this paper, we propose the algorithms of processing the uncertainty data using data fusion for the next generation intrusion detection. In the next generation intrusion detection, a lot of data are collected by many of network sensors to discover knowledge from generating information in cyber space. It is necessary the data fusion process to extract knowledge from collected sensors data. In this paper, we have proposed method to represent the uncertainty data, by classifying where is a confidence interval in interval of uncertainty data through feature analysis of different data using inference method with Dempster-Shafer Evidence Theory. In this paper, we have implemented a detection experiment that is classified by the confidence interval using IRIS plant Data Set for anomaly detection of uncertainty data. As a result, we found that it is possible to classify data by confidence interval.
Data fusion;Dempster-Shafer;Inferential Method;Anomaly Detection;
 Cited by
PIR 센서 기반 침입감지 시스템,정연우;;조성원;정선태;

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