An Efficient Hybrid Intrusion Detection System based on C5.0 and SVM
Now-a-days, 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, the authors develop a hybrid method of C5.0 and SVM and investigate and evaluate the performance of the proposed method with DARPA dataset.