基于推进贝叶斯分类法的入侵检测引擎研究  

Research on Intrusion Detection Engine Based on Boosting Bayesian Classification Algorithm

作　　者：张元清[1] 包骏杰[1] 

机构地区：[1]重庆教育学院计算机与现代教育技术系,重庆400067

出　　处：《计算机科学》2007年第9期87-89,共3页Computer Science

摘　　要：为了提高贝叶斯分类法的准确率,设计了基于推进技术的贝叶斯分类法,并将推进贝叶斯分类法应用到入侵检测引擎中,并设计了基于推进贝叶斯分类的入侵检测引擎。通过实验表明,此检测引擎可以有效的将入侵行为与非入侵行为进行分类,与传统贝叶斯分类法的检测引擎相比,此引擎对数据的分类有更高的准确率。To improve the accurate of Bayesian algorithm, a new Bayesian classification algorithm which based boosting was designed. The new Bayesian algorithm had been used in Intrusion Detection System, and an engine of Intrusion Detection System based the algorithm had been designed. Experiments show that the boosting Bayesian classification algorithm is more accurate than traditional Bayesian classification algorithm based Data Mining detection.

关 键 词：入侵检测 数据挖掘 贝叶斯 推进 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]