文献类型：期刊文献

英文题名：Deep learning based intelligent intrusion detection

作者：Zhang, Xueqin[1]; Chen, Jiahao[1]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China

年份：2017

卷号：2017-January

起止页码：1133

外文期刊名：2017 9th IEEE International Conference on Communication Software and Networks, ICCSN 2017

收录：EI(收录号：20182605383656)

语种：英文

外文关键词：Learning algorithms - Data mining - Deep learning - Intrusion detection - Statistical tests

摘要：To study the characteristics and performance of the deep learning in intelligent intrusion detection, two hybrid algorithms, which combine restricted Boltzmann machine (RBM) with support vector machine (SVM) and deep belief network (DBN) respectively, are used to analyze the accuracy, false positive rate, false negative rate and testing time with the data set used for The Third International Knowledge Discovery and Data Mining Tools Competition (KDDCup99). Compared with each other and traditional hybrid intrusion detection algorithm, DBN performs better than the other both in the accuracy and speed, which is attributed to the unsupervised learning of RBM networks and the combination of the neural networks at the bottom. ? 2017 IEEE.