Modeling an Intrusion Detection System Using Data Mining and Genetic Algorithms Based on Fuzzy Logic

Overview

Fuzzy logic based methods together with the techniques from Artificial Intelligence have gained importance. Data mining techniques like clustering techniques, Association rules together with fuzzy logic to model the fuzzy association rules are being used for classifying data. These together with the techniques of genetic algorithms like genetic programming are producing better results. The present paper proposes a model for intrusion detection systems for anomaly detection based on fuzzy association rules which use genetic programming. The model is implemented and tested on sample data with 40 variables and the results are documented in the paper. As the model includes the LGP,MEP and GEP where the three collectively tries to detect the intrusion to a great extent.