Network Intrusion Detection System Using C4.5 Algorithm

Abstract

There is a great concern about the security of computer these days. The number of attacks has increased in a great number in the last few years, intrusion detection is the main source of information assurance. While firewalls can provide some protection, they fail to provide protection fully and they even need to be complemented with an intrusion detection system (IDS). A newer approach for Intrusion detection is data mining techniques.IDS system can be developed using individual algorithms like neural networks, clustering, classification, etc. The result of these systems is good detection rate and low false alarm rate. According to a recent study, cascading of multiple algorithms gives a way better performance than single algorithm. Single algorithm systems have a high alarm rate. Therefore, to solve this problem, a combination of different algorithms are required. In this research paper, we use the hybrid algorithm for developing the intrusion detection system. C4.5 Support Vector Machine (SVM) and Decision Tree combined to achieve high accuracy and diminish the false alarm rate. Intrusions can be classified into types like Normal, DOS, R2L and U2R.Intrusion detection with Decision trees and SVM were tested with benchmark standard NSL- KDD, which is the extended version of  KDD Cup 1999 for intrusion detection (ID).