An Intrusion Detection Model Based on Deep Belief Networks

摘要：

　　This paper focuses on an important research problem of Big Data classification in intrusion detection system.Deep Belief Networks is introduced to the field of intrusion detection,and an intrusion detection model based on Deep Belief Networks is proposed to apply in intrusion recognition domain.The deep hierarchical model is a deep neural network classifier of a combination of multilayer unsupervised learning networks,which is called as Restricted Boltzmann Machine,and a supervised learning network,which is called as Backpropagation network.The experimental results on KDD CUP 1999 dataset demonstrate that the performance of Deep Belief Networks model is better than that of SVM and ANN.

关键词： Intrusion Detection Deep Belief Networks Restricted Boltzmann Machine

作者: Ni GAO Ling GAO Quanli Gao Hai Wang

作者单位: Dept.of Information Science and Technology,Northwest University Xian,China

会议类型: 国际会议

会议名称: 2014 2nd International Conference on Advanced Cloud and Big Data (CBD 2014)(2014年先进云计算和大数据国际会议)

会议地点: 安徽黄山

会议语种:英文

页码: 247-252

在线出版日期: 2014-11-20（万方平台首次上网日期，不代表论文的发表时间）