| 一种新的入侵检测方法 |
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| 引用本文： | 李昂.一种新的入侵检测方法[J].南阳师范学院学报,2007,6(6):71-73. |
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| 作者姓名： | 李昂 |
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| 作者单位： | 河南大学,计算机与信息工程学院,河南,开封,475001 |
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| 摘 要： | 将一种基于聚类算法的RBF(径向基函数)神经网络方法运用于入侵检测中。在这种方法中采用两阶段学习方法,在利用非监督学习算法确定网络隐层中心时,提出一种基于高斯基的距离度量,并联合输入输出聚类的策略。基于F isher可分离率设计高斯基距离量度中的惩罚因子,可以提高聚类的性能。通过构建入侵检测模型,一方面可以加速网络训练速度,另一方面可以提高入侵检测在预测误报漏报中的性能。
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| 关 键 词： | 径向基函数(RBF)神经网络 聚类 入侵检测 高斯基距离 |
| 文章编号： | 1671-6132（2007）06-0071-03 |
| 修稿时间： | 2006-08-26 |
A new intrusion detection method |
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| LI Ang.A new intrusion detection method[J].Journal of Nanyang Teachers College,2007,6(6):71-73. |
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| Authors: | LI Ang |
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| Abstract: | A method which a new clustering arithmetic RBF neural network is used in the intrusion detection is presented.Two-phase learning is considered in the method.When the network hidden layer centers are confirmed by unsupervised learning arithmetic,a strategybased on Gaussian basis and input-output clustering is presented.Based on Fisher detachable ratio,the punishment gene in Gaussian basis distance is designed,the clustering performance is improved.An intrusion detection model is built according to the method which is used in the paper,one hand,network training speed is enhanced,on the other hand,the intrusion detection performance in misinformation forecast is improved.The simulations result shows that the network has better detection effect. |
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| Keywords: | radial basis function(RBF)neural networks clustering intrusion detection Gaussian basis |
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