粗糙集和决策树方法在IDS中的应用研究

Research on Rough Set Theory and Decision Tree Method Applied to IDS

入侵检测系统(IDS)是数据挖掘的一个热门应用领域。为了解决当前建立的入侵检测系统缺少有效性的问题,文中首先介绍入侵检测系统产生的背景和入侵检测系统的特点,分析决策树归纳学习的过程,从数据挖掘的角度,首先使用粗糙集进行属性约简,运用决策树学习方法对入侵检测数据进行归纳学习。从结果看出粗糙集和决策树学习方法在建立入侵检测系统上的有效性和实用性。

Intrusion detection system（IDS）is a hot application field. In order to solve the problem of lacking of validity on building IDS currently, first introduces the background of appearance of IDS and the character of IDS, analyses the process of decision tree learning. From the angle of data mining, reduces attributes of intrusion detection data with the method of rough set, and then learns the data with the method of decision tree. The result indicates the validity and practicability of rough set and decision tree on building IDS.