FUZZY DATA MINING AND GENETIC ALGORITHMS APPLIED TO INTRUSION DETECTION

Abstract

We are developing a prototype intelligent intrusion detection system (IIDS) to demonstrate the effectiveness of data mining techniques that utilize fuzzy logic and genetic algorithms. This system combines both anomaly based intrusion detection using fuzzy data mining techniques and misuse detection using traditional rule-based expert system techniques. The anomaly-based components are developed using fuzzy data mining techniques. They look for deviations from stored patterns of normal behavior. Genetic algorithms are used to tune the fuzzy membership functions and to select an appropriate set of features. The misuse detection components look for previously described patterns of behavior that are likely to indicate an intrusion. Both network traffic and system audit data are used as inputs for both components.