IDS by Using Data Mining Based on Class-Association-Rule Mining and Genetic Network Programming

Abstract

Now a day’s security is considered as major topics in networks, since the network has increasing widely day by day. Therefore, intrusion detection systems have paid more awareness, as it has an ability to identify intrusion accesses effectively. All these systems can spot the attacks and behave by trigger different errors .The proposed system includes a data mining method with fuzzy logic and class-association rule mining method which is based on genetic algorithm [1]. As the use of fuzzy logic, the recommend system can able to show the different type of features and also able to keep away from the different problems that are arising in to the suggested system approach. By using Genetic algorithm it is possible to find many rules and regulations and that are use to anomaly detection systems an association-rule-mining is very important technique that is used to find valuable rules and these rules are used by different users, instead of to find all the rules meeting the criteria that are useful for detection. Different results that are experimented with KDD99 [9] Cup database realise that the proposed approach gives more detection rates as compared to crisp data mining.
DOI: 10.17762/ijritcc2321-8169.150637