Fuzzy-Association Rule Mining based Intrusion Detection System using Genetic Algorithm

Abstract: Today it is very essential to preserve a high level security to ensure safe and trusted communication of information between various organizations. But due to various threats like intrusions and misuses, secured data communication over internet and any other network is very difficult to achieve. So Intrusion Detection Systems have become a needful component in terms of computer security. An intrusion can be defined as any set of actions that compromise the three main aims of security i.e integrity, confidentiality or availability of a network resource(such as user accounts, file system, kernels & so on). Data mining plays a outstanding role in data analysis. So data mining plays an important role in Intrusion Detection System as it relays upon the auditing of data. These systems identify attacks and react by generating alerts or by blocking the unwanted data/traffic. The proposed work includes fuzzy logic with a data mining method which is a association rule mining method based on genetic algorithm. Due to the use of fuzzy logic, the system can deal with mixed type of attributes and also avoid the sharp boundary problem. Genetic algorithm is used to extract many rules which are required for anomaly detection systems. An association-rule- mining method is used to extract a sufficient number of important rules for the user‟s purpose rather than to extract all the rules meeting the criteria which are useful for misuse detection.

Keywords: Association Rules, Data Mining, Fuzzy logic, Genetic Algorithm, Intrusion Detection System.