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Unsupervised data mining techniques require human intervention for understanding and analysis of the clustering results. This becomes an issue in dynamic users/applications and there is a need for real-time decision making and interpretation. In this paper we will present an approach to automate the annotation of results obtained from data stream clustering to facilitate interpreting that whether...
The 3 most important issues for anomaly detection based intrusion detection systems by using data mining methods are: feature selection, data value normalization, and the choice of data mining algorithms. In this paper, we study primarily the feature selection of network traffic and its impact on the detection rates. We use KDD CUP 1999 dataset as the sample for the study. We group the features of...
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