微型电脑应用2018，Vol.34Issue(6)：36-39,43,5.
基于深度学习的支持向量机的信息安全检测和预警研究
Study on Information Security Detection and Early Warning of Support Vector Machine Based on Deep Learning
王贵喜1
作者信息
- 1. 92493 部队 一分队, 葫芦岛 125001
- 折叠
摘要
Abstract
In order to improve the accuracy and reduce the false alarm rate of network intrusion detection in complex environment, a network information security detection algorithm based on depth belief network (DBN) and support vector machine is proposed. DBN is used to extract a large number of complex network intrusion characteristic attribute data, then SVM is used for network intrusion detection. The results show compared with DBN and SVM, DBN-SVM has higher detection accuracy and lower false alarm rate, and provides a new method for network intrusion detection and early warning.关键词
深度学习/支持向量机/信息安全/深度置信网络/误报率/准确率Key words
Deep learning/Support vector machine/Information security/Deep belief nets/False alarm rate/Accuracy rate分类
矿业与冶金引用本文复制引用
王贵喜..基于深度学习的支持向量机的信息安全检测和预警研究[J].微型电脑应用,2018,34(6):36-39,43,5.
