Information security is one of the cornerstones of Information Society. Integrity and privacy of financial transactions, personal information and critical infrastructure data, all depend on the availability of strong and trustworthy security mechanisms. In recent years, many researchers are using data mining techniques for building IDS. Here, the authors propose a new approach by utilizing data mining techniques such as neuro-fuzzy and radial basis Support Vector Machine (SVM) for helping IDS to attain higher detection rate. The proposed technique has four major steps: primarily, k-means clustering is used to generate different training subsets.