The vast alert generation of IDS in the network is the major problem. It is the vital task to find solutions to reduce the alerts. Novel techniques namely Fuzzy Association rule and Fuzzy art map are proposed to identify attacks optimally and to reduce alerts. The execution time is reduced by placing the level of severity and importance. All alerts that are issued by IDSs are not on the same level of severity and importance. It would be great if the system can identify which alerts are highly important and which are not, so that the number of alerts that need to be dealt with can be reduced. The alert is reduced by finding out the attacks accurately using various methods. The Membership function is used to classify the attack as low, mid or high using continuous attribute. The rules are set for each attack using fuzzy association rule. The chi-square, confidence and support values are estimated for each rule and the minimum value will be set for all parameters .The Rules higher than the verge value are taken and the rules for each generation are updated. Then the rules are compared with test data set and calculated the match degree for each attack. The proposed fuzzy association rule is to obtain superlative features. The Fuzzy art map technique is used to classify the intrusion and normal data by calculating the match degree. Hence this technique aims to effectively reduce the alert rate when compared with existing approaches.
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