Anomaly Detection in Network Security Based on Nonparametric Techniques

In this paper, we propose a hybrid feature selection method in which Principal Components Analysis is combined with optimized k- Means clustering technique. Our approach hierarchically reduces the redundancy of features with high explanation in PCA for choosing a good subset of features critical to improve the performance of classifiers. Based on this result, we evaluate the performance of intrusion detection by using a nonparametric density estimation approach based on Parzen-Window and k-Nearest Neighbor classifiers over data sets with reduced features. The experiment with KDD Cup 1999 data set show several advantages in terms of computational complexity and our method achieves significant detection rate which shows possibility of detecting successfully attacks.