A Hybrid Approach for Intrusion Detection using Misuse Detection and Genetic Algorithm

Abstract

Network Security has become the crucial issue for most of the organizations in the recent past. Mostly discussions on security include the tools and methods that can be deployed to protect and defend the networks. The use of network security tools have increased over the years due to increase in security threats. Many methods have been developed to secure computer networks and communication over the Internet. In today’s fast-changing Information technology world, even the best available security is deficient for the latest vulnerabilities. In order to protect data and system integrity, Intrusion Detection has become central area for researchers. Intrusion detection method is one such method which has gained importance over the past few years. In this dissertation, we have proposed an algorithm based on combination of Misuse Detection and Genetic Algorithm approach. We are using feature selection technique to extract important features from dataset. Genetic Algorithm is used for evolving best fit rules; this algorithm works on the principle of survival of fittest. For training and testing the rules, KDD Cup'99 datasets are used. The results of Misuse Detection and Proposed System are compared on various parameters like detection rates, false positive rates and number of attacks detected. Results prove that proposed approach has better detection rates and low false positive rates than Misuse Detection. Proposed System detects ten different types of attacks with high detection rates and low false positive rates. This System is also compared with existing systems which were described in research papers and results shows that our system gives less false positive rates than existing systems.