Neural Network Ensembles for Intrusion Detection

The major problem of existing models is recognition of new attacks, low accuracy, detection time and system adaptability. In this paper the method of recognition of attack class on the basis of the analysis of the network traffic is described. Our first approach is based on combination principal component analysis (PCA) neural networks and multilayer perceptrons (MLP). The second approach performs recognition of a class of attack by means of the cumulative classifier with nonlinear recirculation neural networks (RNN) as private detectors. The proposed approaches are tested using KDD-99 dataset. The experimental results demonstrate that the designed models are promising in terms of accuracy and computational time for real world intrusion detection.