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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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 Abstract
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.
 Keywords
Data fusion;Dempster-Shafer;Inferential Method;Anomaly Detection;
 Language
Korean
 Cited by
 References
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