By Florez G., Bridges S.M., Vaughn R.B.
Now we have been utilizing fuzzy facts mining concepts to extract styles that symbolize common habit for intrusion detection. during this paper we describe numerous adjustments that we have got made to the information mining algorithms for you 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 realize anomalous habit, wegenerate fuzzy organization principles from new audit information and compute the similarity with units mined from "normal" info. If the similarity values are lower than a threshold worth, an alarm isissued. during this paper we describe an set of rules for computing fuzzy organization ideas according to Borgelt's prefix timber, alterations to the computation of aid and self belief offuzzy principles, a brand new strategy for computing the similarity of 2 fuzzy rule units, and have choice and optimization with genetic algorithms. Experimental effects reveal that we will be able to in achieving higher operating time and accuracy with those differences.
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