计算机科学与探索2019，Vol.13Issue(7)：1124-1134,11.
深度学习模型下多分类器的入侵检测方法
Intrusion Detection Method of Multiple Classifiers Under Deep Learning Model*
摘要
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.关键词
入侵检测/深度学习/反向传播神经网络/梯度提升树Key words
intrusion detection/ deep learning/ back propagation neural network/ gradient boosting decision tree分类
信息技术与安全科学引用本文复制引用
CHEN Hong,CHEN Jianhu+,XIAO Chenglong,WAN Guangxue,XIAO Zhenjiu..深度学习模型下多分类器的入侵检测方法[J].计算机科学与探索,2019,13(7):1124-1134,11.基金项目
The National Natural Science Foundation of China under Grant No. 61404069 (国家自然科学基金). （国家自然科学基金）
