基于粗糙集和概念格的入侵检测研究
摘要
随着社会网络化程度的增加，安全性隐患日益明显地开始暴露出来，近年来入侵事件的数目已成指数速度增长。入侵检测系统作为计算机安全防御体系的一个重要组成部分正越来越受到人们重视。
入侵检测是对(网络)系统的运行状态进行监视，发现各种攻击企图、攻击行为或者攻击结果，以保证系统资源的机密性、完整性与可用性。一般地，根据数据分析方法(检测方法)的不同，可以将入侵检测分为异常检测模型与滥用检测模型两种。
本文通过对训练数据进行学习，得到正常行为与异常行为的规则集，建立了一个基于规则分类判决的入侵检测模型系统，并通过实验验证了此系统的可用性。基于规则分类判决的入侵检测模型系统优点是：如果提取的规则侧重于正常行为，则可得到正常行为的规则集，以构造异常检测模型；若关注的是异常行为，可形成关于入侵行为的特征集，然后可以监视网络，查找入侵行为(滥用检测)。
模型关键在于决策规则的获取，即通过对训练数据进行学习，得到正常行为与异常行为的规则集。由于粗糙集理论提供了一整套比较成熟的在样本数据集中寻找和发现数据属性之间关系的方法，能更好地描述从有限样本中反映出来的属性之间关系的本质特征，在我们的模型中综合运用了粗糙集与概念格、信息论的有关思想，先采用条件熵的方法对属性进行约简。然后又将概念格和变精度粗糙集相结合，提出了决策规则格的概念，并在构造算法中采用了三种剪枝策略，得到β-下近似决策规则格。最后从此决策规则格中提取出相应的β-一致的决策规则。
实验表明，我们提取出的决策规则有了极大的简化，规则的适应性更强。
入侵检测是对(网络)系统的运行状态进行监视，发现各种攻击企图、攻击行为或者攻击结果，以保证系统资源的机密性、完整性与可用性。一般地，根据数据分析方法(检测方法)的不同，可以将入侵检测分为异常检测模型与滥用检测模型两种。
本文通过对训练数据进行学习，得到正常行为与异常行为的规则集，建立了一个基于规则分类判决的入侵检测模型系统，并通过实验验证了此系统的可用性。基于规则分类判决的入侵检测模型系统优点是：如果提取的规则侧重于正常行为，则可得到正常行为的规则集，以构造异常检测模型；若关注的是异常行为，可形成关于入侵行为的特征集，然后可以监视网络，查找入侵行为(滥用检测)。
模型关键在于决策规则的获取，即通过对训练数据进行学习，得到正常行为与异常行为的规则集。由于粗糙集理论提供了一整套比较成熟的在样本数据集中寻找和发现数据属性之间关系的方法，能更好地描述从有限样本中反映出来的属性之间关系的本质特征，在我们的模型中综合运用了粗糙集与概念格、信息论的有关思想，先采用条件熵的方法对属性进行约简。然后又将概念格和变精度粗糙集相结合，提出了决策规则格的概念，并在构造算法中采用了三种剪枝策略，得到β-下近似决策规则格。最后从此决策规则格中提取出相应的β-一致的决策规则。
实验表明，我们提取出的决策规则有了极大的简化，规则的适应性更强。
引文
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