摘要
由于网络安全设备日志特征繁多,有些特征对网络状态的分析不起作用或作用很小,而且这些特征的存在会加大数据分析的时间开销。而基于云模型和粗糙集的特征选择算法,首先通过云模型对入侵检测系统日志属性进行重要性排序,其结果可再根据专家的意见进行适当的调整,从而保证属性序的给定既符合数据的实际分布,又能满足对属性偏好的需求。接着,根据给定的属性序进行约简,自动选择有效的特征子集。实验的结果证明,该算法能快速得到特征选择的结果,并且达到较高水平的分类效果。
The network security log has a lot of attributes,and some of them have no effects or few effects on network situation analysis,which may cause a waste of time. This paper presents an algorithm of feature selection based on cloud model and rough set. Cloud model is applied to sort the importance of log attributes in intrusion detection system,the result of which can be adjusted by the opinion of experts,guaranteeing the attribute order to accord with the actual distribution of data and satisfy the demand of attribute preference,making the attribute reduction of rough set theory to reduce the dimension of data vector,automatically selecting the effective feature subset according to the given attribute order. The results show that the algorithm is feasible and efficient.
出处
《东莞理工学院学报》
2015年第5期57-61,共5页
Journal of Dongguan University of Technology
关键词
特征选择
云模型
粗糙集
属性约简
feature selection
cloud model
rough set
attribute reduction
