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With the impending era of internet, the network security has become the key foundation for lot of financial and business web applications. Intrusion detection is one of the looms to resolve the problem of network security. Imperfectness of intrusion detection systems (IDS) has given an opportunity for data mining to make several important contributions to the field of intrusion detection. In recent years, many researchers are using data mining techniques for building IDS. Here, we 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. Then, based on the obtained training subsets, different neuro-fuzzy models are trained. Subsequently, a vector for SVM classification is formed and in the end, classification using radial SVM is performed to detect intrusion has happened or not. To illustrate the applicability and capability of the new approach, the results of experiments on KDD CUP 1999 dataset is demonstrated. Experimental results shows that our proposed new approach do better than BPNN, multiclass SVM and other well-known methods such as decision trees and Columbia model in terms of sensitivity, specificity and in particular detection accuracy.