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In this paper, we propose a fuzzy clustering model to find the proper cluster structures from a dataset used for intrusion detection. Since, genetic algorithm is an effective technique to improve the classification accuracy. In this paper, we propose a Novel Weighted Fuzzy C-Means clustering method based on Immune Genetic Algorithm (IGA-NWFCM) and hence it improves the performance of the existing techniques to solve the high dimensional multi-class problems. Moreover, the probability of obtaining the global optimal value is increased by the application of immune genetic algorithm. This proposed algorithm provides a high classification accuracy, stability and probability of gaining global optimum value. The experimental results obtained from this work shows that the clustering results and the proposed algorithm provides better classification accuracy when tested with KDD’99 cup data set.