Wavelet neural network model for network intrusion detection system

Abstract

Network Intrusion Detection is the process of analyzing the network traffic so as to unearth any unsafe and possibly disastrous exchanges happening over the network. In the nature of guaranteeing the confidentiality, availability, and integrity of any networking system, the accurate and speedy classification of the transactions becomes indispensable. The potential problem of all the Intrusion Detection System models at the moment, are lower detection rate for less frequent attack groups, and a higher false alarm rate. In case of networks and simulation works signal processing has been a latest and popular technique. In this study, a hybrid method based on coupling Discrete Wavelet Transforms and Artificial Neural Network (ANN) for Intrusion Detection is proposed. The imbalance of the instances across the data-set was eliminated by SMOTE based oversampling of less frequent class and random under-sampling of the dominant class. A three-layer ANN was used for classification. The experimental results on KDD99 data-set advocate about the fact that the proposed model has higher accuracy, detection rate and at the same time has reduced false alarms making it suitable for real-time networks.