An Improved Algorithm for Fuzzy Data Mining for Intrusion by Florez G., Bridges S.M., Vaughn R.B.

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By Florez G., Bridges S.M., Vaughn R.B.

We've been utilizing fuzzy info mining ideas to extract styles that characterize general habit for intrusion detection. during this paper we describe a number of differences that we have got made to the information mining algorithms to be able to enhance accuracy and potency. We use units of fuzzy organization ideas which are mined from community audit information as versions of "normal habit. To notice anomalous habit, wegenerate fuzzy organization ideas from new audit info and compute the similarity with units mined from "normal" info. If the similarity values are under a threshold worth, an alarm isissued. during this paper we describe an set of rules for computing fuzzy organization principles in line with Borgelt's prefix bushes, alterations to the computation of aid and self belief offuzzy principles, a brand new procedure for computing the similarity of 2 fuzzy rule units, and have choice and optimization with genetic algorithms. Experimental effects display that we will be able to in attaining greater operating time and accuracy with those changes.

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