A Hybrid Intrusion Detection System for Contemporary Network Intrusion Dataset

We propose a hybrid intrusion detection approach to detect network anomalies. The proposed approach uses a feature discrete method and a cluster analysis algorithm to separate the training samples into two groups, normal and anomaly groups, and then a new classification model is built to improve the performance of the sub group classification. We discretize the features of training samples by the method considering the interdependence between features and labels. Class information is added into the attributes to enhance the clustering results. For the anomaly group, several representative features are selected to construct a classification model to improve the overall classification performance. Two efficient machine learning algorithms, the Decision Tree algorithm and the Bayesian Network algorithm, are adopted in our experiment. The experiment results show that our method can increase both the normal and anomaly detection rate, precision and accuracy. For the classification of new types of modern attacks, our approach also can improve the overall accuracy.