Real-time intrusion detection with Genetic, Fuzzy, Pattern matching algorithm

Intrusion detection system classifies network behaviors into two main activities: normal and suspicious activities by monitoring activities. This paper represents the approach of detecting intrusion in network using Genetic, Fuzzy and Pattern matching algorithm. Fuzzy rule, which is a machine learning algorithm, is used for identifying the intrusion behavior within in a network. Genetic algorithm provides best optimal solution by using collection of genetic parameters like mutation, crossover and selection of best fit chromosomes. Pattern matching algorithm provide best detection rule to detect attack. Our approach can be implemented on both dataset i.e. KDDCUP99 and our own network dataset. Intrusion detection system will be evaluated in terms of detection rate, detection speed and false alarm rate and attack types.