ZHANG Jian. Intrusion detection based on cloud model and BP neural networks[J]. 2011, 30(1): 116-118+122. DOI: 10.13873/j.1000-97872011.01.028.
基于云模型和BP神经网络的入侵检测
摘要
云模型是进行定性定量转换的有效工具
正态云模型通过期望、熵和超熵构成特定结构发生器
这种特定结构使得正态云模型更具有普遍适用性
更简单、直接地完成了定性与定量之间的相互转换过程。结合BP网络局部精确搜索的特性
提出一种基于云模型与BP网络的入侵检测方法
将云模型和BP算法有机结合。通过KDD99 CUP数据集分别进行了仿真实验对比
结果表明:该方法不但收敛速度快
迭代次数较少
并且可提高入侵检测的准确率。
Abstract
Cloud model is an effective tool for qualitative and quantitative transform
a specific structure generator is formed by normal cloud model through expectation
entropy and hyper entropy
This particular structure makes the normal cloud model has more general applicability and simply and straightforward completed conversion process between qualitative and quantitative.An intrusion detection methods is proposed based on cloud model and BP Network.The cloud model and BP algorithm are combined.The simulation experiment is compared respectively by KDD99 CUP data set.Experimental results show that the method is not only fast convergence but also has fewer number of iterations.It can improve the accuracy of intrusion detection.