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Java,.Net,Android,Testing,Oracle,user’s purpose rather than to extract all the rulesmeeting the criteria.Like most of the existing association-rule mining algorithms,conventionalassociation-rule mining based on GNP is able to extract ruleswith attributes of binary values. However, in real-world applications,databases are more likely to be composed of both binary and continuousvalues.This paper describes a novel fuzzy class-association-rule miningmethod based on GNP and its application to intrusion detection. Bycombining fuzzy set theory withGNP,theproposed method can dealwith the mixed database that contains both discrete ndcontinuousattributes. Such mixed database is normal in real-world applicationsand GNP can extract rules that include both discrete and continuous
attributes consistently. The initiative of combining associationrule
mining with fuzzy set theory has been applied more frequently
in recent years. The original idea comes from dealing with quantitative
attributes in a database, where discretization of the quantitative
attributes into intervals would lead to under- or overestimate the
values that are near the borders. This is called the sharp boundary
problem. Fuzzy sets can help us to overcome this problem by allowing
different degrees of memberships. Compared with traditional
association rules with crisp sets, fuzzy rules provide good linguistic
explanation

HARDWARE:.
Here, the concept of GNP-based fuzzy class-association-rule mining
is introduced in detail. The fuzzy membership values are used for
fuzzy rule extraction, and subattribute-utilization mechanism is proposed
to avoid the information loss. Meanwhile, a new GNP structure
for association-rule mining is built up so as to conduct the rule extraction
step. In addition, a new fitness function that provides the flexibility
of mining more new rules and mining rules with higher accuracy is
given in order to adapt to different kinds of detection. After the extraction
of class-association rules, these rules are used for classification.
In this paper, two kinds of classifiers are built up for misuse detection
and anomaly detection, respectively, in order to classify new data
correctly.
For misuse detection

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