基于Boosting RBF神经网络的入侵检测  

Intrusion detection based on boosting method and RBF neural network

作　　者：党长青[1] 刘洁[1] 牛分中[1] 

机构地区：[1]唐山学院信息工程二系,河北唐山063020

出　　处：《计算机工程与应用》2008年第15期118-120,共3页Computer Engineering and Applications

基　　金：河北省自然科学基金(the Natural Science Foundation of Hebei Province of China under Grant No.F2007000682)

摘　　要：提出一种新颖的基于boosting RBF神经网络的入侵检测方法。将模糊聚类和神经网络技术相结合,提出基于改进的FCM算法和OLS算法相结合的FORBF算法,为了提高RBF神经网络的泛化能力,采用Boosting方法,进行网络集成。以"KDD Cup 1999 Data"网络连接数据集训练神经网络并仿真实验,得到了较高的检测率和较低的误警率。A novel method is proposed for intrusion detection based on improved boosting RBF neural network.By combining fuzzy clustering and neural network,this paper puts forward an FORBF algorithm based on improved FCM and OLS.In order to improve the precision of the RBF neural network for intrusion detection,the improved boosting algorithm is used to build an integration-neural network.Simulated experiments with KDD Cup 1999 network connections data have shown that the proposed method is effective for intrusion detection owing to excellent performance of the higher attack detection rate with lower false positive rate.

关 键 词：入侵检测 BOOSTING方法 RBF神经网络 正交最小二乘法 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]