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Performance Analysis of Classifiers for Intrusive Data and Rough Sets Reducts


R.Ravinder Reddy, E. Padmalatha, Y.Ramadevi, K.V.N Sunitha


Vol. 14  No. 8  pp. 103-107


The fast change in the day to day activity need to analyze the intrusive data very accurately without losing performance. Intrusive behavior is critical for analyzing the data and Performance is crucial in the computational environment, when the user requires accuracy in the results. Finding intrusive behavior in the network with accuracy and speed is critical. In this paper we try to find out intrusive behavior more faster manner without losing the accuracy and improving the performance of the classifier. First we analyze the different characteristics of the classifiers and then we find out the reducts for the KDDCUP99 data set using the rough set theory and minimize the data set without losing any decisive attribute and prepare the new data set. With the new dataset we conducted the experiments for selected classification algorithms in data mining for both the datasets and compare performances


Data mining, classification, Rough sets, Intrusion detection