An Efficient Hybrid Intrusion Detection System based on C5.0 and SVM

Abstract

Nowadays, much attention has been paid to intrusion detection system (IDS) which is closely linked to the safe use of network services. Several machine-learning paradigms including neural networks, linear genetic programming (LGP), support vector machines (SVM), Bayesian networks, multivariate adaptive regression splines (MARS) fuzzy inference systems (FISs), etc. have been investigated for the design of IDS. In this paper, we develop a hybrid method of C5.0 and SVM and investigate and evaluate the performance of our proposed method with DARPA dataset. The motivation for using the hybrid approach is to improve the accuracy of the intrusion detection system when compared to using individual SVM and individual SVM.