Network intrusion detection based on IE-DBN model

Existing network intrusion detection models suffer such problems as low detection accuracy and high false alarm rates in face of massive data traffic. Deep-learning models provide a solution as they can reduce the dimensionality of massive data, extract data features, and identify intrusions. However, the network structure and the number of hidden layer neurons of deep-learning models are determined by empirical or trial-and-error methods, which will affect the generalization ability and learning efficiency of the model. In the present work, a deep belief network model based on information entropy (IE-DBN model) is proposed for network intrusion detection. The model uses information gain (IG) to reduce the dimensionality of high-dimensional data features and remove redundant features. The information entropy is used to determine the number of hidden neurons in the DBN network and the network depth. The synthetic minority oversampling technique (SMOTE) algorithm is used to address the problem of data imbalance. Tests on the KDD CUP 99 intrusion detection data set have shown that the proposed IE-DBN model improved the convergence speed of the model and reduced the likelihood of overfitting. Compared with the conventional back propagation (BP) neural network and DBN network model, the IE-DBN model obtained a higher detection accuracy and a lower false alarm rate. Verification tests on other intrusion detection data sets showed that the proposed IE-DBN model had good generalization capacity.

Automated summary

The proposed IE-DBN model based on information entropy aims to improve network intrusion detection by reducing data dimensionality, optimizing network structure, and addressing issues in deep learning models. Tests have shown that the IE-DBN model outperforms traditional neural network models in terms of accuracy and false alarm rates.