文献类型：会议论文

英文题名：Deep Learning Based Intelligent Intrusion Detection

作者：Zhang, Xueqin[1];Chen, Jiahao[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai, Peoples R China

会议论文集：9th IEEE International Conference on Communication Software and Networks (ICCSN)

会议日期：MAY 06-08, 2017

会议地点：GuangZhou, PEOPLES R CHINA

语种：英文

外文关键词：intrusion detection; deep belief networks; restricted Boltzmann machines; support vector machines

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.