摘要
提出一种基于RBF和Elman混合神经网络模型的入侵检测系统.本模型具有记忆功能,可以有效地检测离散而又相联系的攻击行为.RBF网络是一个实时的模式分类器,而Elman网络实现了对事件的记忆能力.基于此混合模型的入侵检测系统使用DARPA数据集进行测试评估,使用ROC曲线直观的显示测试的结果.实验证明基于此混合模型的入侵检测系统可以有效地提高检测率,降低误报率和漏报率.
This paper proposes an intrusion detection model based on RBF and Elman Hybrid Neural Network model. This model has the memory function, can effective detection discrete and linked to attack. This model has the memory function, can effective detection discrete and linked to attack. The RBF network is a real-time mode classifier, and the Elman network can remember events. This system USES DARPA data set to test and evaluation. Use ROC curves to display of the test results. The experiment proves this system can effectively improve the detection rate, reduce false alarm rate and missed alarm rate.
出处
《微电子学与计算机》
CSCD
北大核心
2009年第8期155-157,162,共4页
Microelectronics & Computer
基金
黑龙江省计算机应用技术重点学科项目(081203)
黑龙江省智能教育与信息工程重点实验室项目
黑龙江省教育厅科研项目(11521069)
