An Effective Integrated Intrusion Detection Model Based on Deep Neural Network

Abstract

With the rapid development of big data and cloud computing, network security threats are also growing. More and more researchers pay attention to the study of intrusion detection algorithms. Traditional intrusion detection algorithms are often unable to detect attacks with high dimensional and imbalanced data as input training data. In order to solve the problem above, this paper proposes an integrated intrusion detection model based on deep neural network. Furthermore, model integration solves the problem of sample imbalance and improves the generalization ability of the model. In this paper, we firstly use Generative Adversarial Networks(GAN) model to sample dataset. Then, multiple deep neural network (DNN) classifiers are established and special screening of the classifiers was carried out. Afterwards, all DNN classifiers were integrated based on AdaBoost integration algorithm. During the training of DNN classifiers, the training samples are sampled through an antagonistic generation network, which reduce the impact of data imbalance on classification performance of DNN classifiers. Finally, by conducting experiments with KDD 99 and NS-KDD data sets, the good stability and high accuracy of proposed model are verified.