Can I read An Improved Algorithm for Fuzzy Data Mining for Intrusion Detection on EtoBox?

An Improved Algorithm for Fuzzy Data Mining for Intrusion Detection by Florez G., Bridges S.M., Vaughn R.B. is a nonfiction available to read on EtoBox.

What is An Improved Algorithm for Fuzzy Data Mining for Intrusion Detection about?

We have been using fuzzy data mining techniques to extract patterns that represent normal behavior for intrusion detection. In this paper we describe a variety of modifications that we have made to the data mining algorithms in order to improve accuracy and efficiency. We use sets of fuzzy association rules that are mined from network audit data as models of "normal behavior. To detect anomalous behavior, wegenerate fuzzy association rules from new audit data and compute the similarity with sets mined from "normal" data. If the similarity values are below a threshold value, an alarm isissued. In this paper we describe an algorithm for computing fuzzy association rules based on Borgelt's prefix trees, modifications to the computation of support and confidence offuzzy rules, a new method for computing the similarity of two fuzzy rule sets, and feature selection and optimization with genetic algorithms. Experimental results demonstrate that we can achieve better running time and accuracy with these modifications.

Who reads An Improved Algorithm for Fuzzy Data Mining for Intrusion Detection?

Author

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

Published

2004

Language

EN

Category

nonfiction

Subjects

Computer Science, Algorithms And Data Structures, Stem