Detecting Network Anomalies in Mixed-Attribute Data Sets
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...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of the Third International Conference on Knowledge Discovery and Data Mining|
|01_Tran_Detecting_Network_Anomalies_in_2010.pdf||174.03 kB||Adobe PDF||Request a copy|
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