IDuFG: Introducing an Intrusion Detection using Hybrid Fuzzy Genetic Approach

In this paper, we propose a hybrid approach for designing Intrusion Detection Systems. This approach is based on a Fuzzy Genetic Machine Learning Algorithm to generate fuzzy rules. The rules are able to solve the classification problem in designing an anomaly IDS. The proposed approach supports multiple attack classification. It means that, it is able to detect five classes consist of Denial of Service, Remote to Local, User to Root, Probing and normal classes. We present a two-layer optimization approach based on Pittsburgh style and then combine it with Michigan style. To improve the performance of the proposed system, we take advantages of memetic approach and proposed an enhanced version of the system. We test it on NSL KDD data set to be able to compare our works with previous ones. As results show our approach can converge faster to the classification accuracy about 98.2% and 0.5% false alarm.