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We propose a practical and predictive security model for intrusion detection in a computer networking environment using data mining. This model uses a classification and regression technique for data mining. The goal of the proposed model is to identify significant variables that measure network intrusion from a wealth of raw network data and perform an efficient vulnerability evaluation based on those variables. Analysis of experimental results conducted using the DARPA benchmark dataset shows that the CART (classification and regression trees) approach performs better compared to other models, like random projection and principal component analysis. The results also indicate that the performance of the CART approach in the proposed model is not significantly affected, even as the dimension of the input data decreases, without compromising the prediction success rate.