基于文化算法的层次属性约减入侵检测模型

Hierarchical Attribute Reduction Model for Intrusion Detection Based on Cultural Algorithm

为有针对性地区分入侵攻击类别,提高入侵检测系统(IDS)整体的分类准确率,提出一种层次属性约减模型。该模型采用文化算法的双层进化思想,结合粗糙集和遗传算法进行属性约减。对数据进行预处理并分层划分子空间,形成决策子表规则集f_D。运用文化算法在信念空间进行知识更新,并将层次评价知识库的进化数据传入种群空间。在种群空间利用粗糙集和遗传算法进行进化和约减,得到各层的优选属性集f_(opt),设计出层次Bayes分类器验证模型性能。实验结果表明,该模型可将属性约减前的Bayes分类正确率提高至98.21%,并能较好地识别出流量特征不明显的R2L,U2R类别的入侵攻击。

In order to distinguish detection attack categories pertinently and improve the classification accuracy of Intrusion Detection System（ IDS）,a hierarchical attributes reduction model used for IDS is proposed. This model reduces attributes by adopting dual structure of culture algorithms and combined with rough sets as well as genetic algorithm.Firstly,the data is preprocessed and divide it into hierarchies,which forms the rule of decision subset f_D. Secondly,using cultural algorithm,knowledge is updated in belief space,and evolving data of hierarchical evaluation knowledge is introduced into population space. Thirdly,the optimal subser of each layer is acquired by using rough sets and genetic algorithm which can evolve knowledge and reduce attributes in belief space. Finally,model performance is verified by designing Bayes hierarchical classifier. Experimental results showthat the algorithm can improve the accuracy of the Bayes classification before attributes reducting to 98. 21%,and it is better to identify the intrusion categories whose traffic characteristics is not obvious such as R2L and U2R.