Detection and Removal of Cracks in Digitized Color Paintings

Network intrusion detection systems (NIDSs) have become an important component in network security infrastructure. Currently, many NIDSs are rule-based systems whose performances highly depend on their rule sets. Unfortunately, due to the huge volume of network traffic, coding the rules by security experts becomes difficult and time consuming. Since, data mining techniques can build intrusion detection model adaptively, data mining based NIDSs have significant advantages over the rule-based system. Therefore, we apply Association rule mining for network intrusion detection. Its mining algorithms discover all item associations (or rules) in the data that satisfy the user- specified minimum support (minsup) and minimum confidence (minconf) constraints. In many applications, association rules will only be interesting if they represent non-trivial correlations between all constituent items. In this paper, Apriori and predictive Apriori methodologies are used for discovering the significant rules. We also report the experimental results over KDDCup'99 datasets. Finally, the result analysis shows that the proposed approach provides the most significant rules, thereby providing strict control over false discoveries.