• 网络与信息安全 •

### 深度学习模型下多分类器的入侵检测方法

1. 辽宁工程技术大学 软件学院，辽宁 葫芦岛 125105
• 出版日期:2019-07-01 发布日期:2019-07-08

### Intrusion Detection Method of Multiple Classifiers Under Deep Learning Model

CHEN Hong, CHEN Jianhu+, XIAO Chenglong, WAN Guangxue, XIAO Zhenjiu

1. College of Software, Liaoning Technical University, Huludao, Liaoning 125105, China
• Online:2019-07-01 Published:2019-07-08

Abstract: Aiming at the problem of poor performance of traditional intelligent intrusion detection methods in massive data environment, a multi-classifier intrusion detection method (DBN-OGB) based on one-versus-one gradient boosting decision tree (GBDT) under deep belief networks (DBN) is proposed. This method first uses the deep belief network to extract the low dimension and representative characteristic data from the high-dimensional and complex intrusion detection data. Then, one-versus-one method is used to construct a gradient tree classifier between two kinds of characteristic data. The classifiers are used to identify the unknown network attack, and the category with the most votes is the category of the attack. Finally, the NSL-KDD data set is used to carry out simulation experiments. The experimental results show that the average accuracy and detection rate of the DBN-OGB method are higher than 99%. Compared with the DBN-MSVM method, the accuracy and detection rate of the method are increased by 0.56% and 1.03% respectively, indicating that DBN-OGB is an effective and feasible intrusion detection method, and can improve the detection performance of massive intrusion data.