I need to see how the KDD 99 was handled in overall manner, specially preprocessing, classification, and clustering algorithms.

Best Regards,
Contriver

Abstract

In recent years, the security has become a critical part of any industrial and organizational information systems. The intrusion detection system is an effective approach to deal with the problems of networks and so different classifiers are used to detect the different kinds of attacks. In this paper, the performance of intrusion detection with various neural network classifiers is compared. In this proposed research there are five types of classifiers used. They are Feed Forward Neural Network (FFNN), Elman Neural Network (ENN), Generalized Regression Neural Network (GRNN), Probabilistic Neural Network (PNN) and Radial Basis Neural Network (RBNN). In this problem, the feature reduction techniques are used to a given KDD Cup 1999 dataset. The performance of the full featured KDD Cup 1999 dataset is compared with that of the reduced featured KDD Cup 1999 dataset. The MATLAB software is used to train and test the dataset and the efficiency is measured. Using the above said technique, it is proved that the reduced dataset is performing better than the full featured dataset.