Intrusion Detection System using Fuzzy Genetic Approach

Abstract: 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 modeled as a fuzzy rule such as "if-then" and improved by a genetic algorithm. 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.

Keywords: genetic algorithm, fuzzy logic, classification, intrusion detection, DARPA data set