Network security is of primary concerned now days for large organizations. The intrusion detection systems (IDS) are becoming indispensable for effective protection against attacks that are constantly changing in magnitude and complexity. With data integrity, confidentiality and availability, they must be reliable, easy to manage and with low maintenance cost. Various modifications are being applied to IDS regularly to detect new attacks and handle them. This paper proposes a fuzzy genetic algorithm (FGA) for intrusion detection. The FGA system is a fuzzy classifier, whose knowledge base is modelled as a fuzzy rule such as "if-then" and improved by a genetic algorithm. The reasons for introducing fuzzy logic is twofold, the first being the involvement of many quantitative features where there is no separation between normal operations and anomalies. Thus fuzzy association rules can be mined to find the abstract correlation among different security features. The method is tested on the benchmark KDD'99 intrusion dataset and compared with other existing techniques available in the literature. The results are encouraging and demonstrate the benefits of the proposed approach.
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