2010 3rd International Conference on Knowledge Discovery and Data Mining (WKDD 2010) (2010)
Jan. 9, 2010 to Jan. 10, 2010
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/WKDD.2010.96
Detecting network anomalies is important part of intrusion detection systems that have been developed with great successes on homogeneous data. There have been successes with mixed-attribute data using various techniques, however, few of them exist for using mixed-attribute data without further manipulation or consideration of dependencies among the different types of attributes. We propose in this paper a fusion of decision tree and Gaussian mixture model (GMM) to detect anomalies in mixed-attribute data sets. Evaluation experiments were performed on the popular KDDCup 1999 data set using C4.5 decision tree, GMM and the fusion of C4.5 and GMM.
decision trees, Gaussian processes, security of data
K. Tran and H. Jin, "Detecting Network Anomalies in Mixed-Attribute Data Sets," 2010 3rd International Conference on Knowledge Discovery and Data Mining (WKDD 2010)(WKDD), Phuket, 2010, pp. 383-386.