计算机应用研究2018，Vol.35Issue(4)：1197-1200,4.DOI:10.3969/j.issn.1001-3695.2018.04.050
海量数据环境下用于入侵检测的深度学习方法
Deep learning method for intrusion detection in massive data
摘要
Abstract
In order to solve the problem that intrusion massive data is not effectively classified using traditional machine learning methods,this paper proposed an intrusion detection method of multi-class support vector machine based on deep belief nets (DBN-MSVM).Firstly,it employed deep belief nets to reduce the feature dimension of large amounts of nonlinear high-dimensional unlabeled input data,and obtained the optimal low-dimensional representation of raw data.Secondly,it used a binary tree structure multi-class support vector machine classifier to recognize intrusion from the optimal low-dimensional data.Finally,experimental results demonstrate that the DBN-MSVM method can reduce the training time and testing time of support vector machine classifier and raise classification accuracy of intrusion massive data on KDD'99 dataset.关键词
入侵检测/深度学习/支持向量机/深度信念网络Key words
intrusion detection/deep learning/support vector machine (SVM)/deep belief nets (DBN)分类
信息技术与安全科学引用本文复制引用
高妮,贺毅岳,高岭..海量数据环境下用于入侵检测的深度学习方法[J].计算机应用研究,2018,35(4):1197-1200,4.基金项目
国家自然科学基金资助项目(61373176,61572401) （61373176,61572401）
国家教育部人文社会科学研究青年项目(16XJC630001) （16XJC630001）
陕西省自然科学基金资助项目(2015JQ7278) （2015JQ7278）
陕西省教育厅科学研究项目(17JK0304,14JK1693) （17JK0304,14JK1693）
