贝叶斯分类算法在入侵检测中的应用

Application of Bayes Classification Algorithm in Intrusion Detection

本文在分析了传统的朴素贝叶斯分类基础上,提出一种改进的贝叶斯分类算法,旨在改进传统贝叶斯分类入侵检测系统模型在检测率、检测时间上不足的问题。在此之后,提出了一种基于粗糙集理论依赖度的属性简约方法,以达到降低属性复杂度、删除冗余属性,使整个检测系统的建模时间有所降低。

This paper is based on traditional Bayes classification algorithm, proposed an improved Bayse Classification algorithm, aims at improving the traditional Bayes classification of intrusion detection system model in the detection rate, detection time. After this, put forward a kind of attribute reduction method based on the rough set theory, in order to reduce the complexity of the attribute, delete redundant attributes, reducing the time of modeling of the system.