基于改进朴素贝叶斯算法的入侵检测系统

计算机科学 ›› 2014, Vol. 41 ›› Issue (4): 111-115.

基于改进朴素贝叶斯算法的入侵检测系统

王辉,陈泓予,刘淑芬

河南理工大学计算机科学与技术学院焦作454000;河南理工大学计算机科学与技术学院焦作454000;吉林大学计算机科学与技术学院长春130012

出版日期:2018-11-14 发布日期:2018-11-14
基金资助:
本文受国家自然科学基金项目(51174263),教育部博士点基金项目(20124116120004),河南省教育厅科学技术研究重点项目(13A510325)资助

Intrusion Detection System Based on Improved Naive Bayesian Algorithm

WANG Hui,CHEN Hong-yu and LIU Shu-fen

Online:2018-11-14 Published:2018-11-14

摘要/Abstract

摘要： 随着互联网连通性的不断增强以及网络流量的日益增大,最近频繁发生的入侵事件再度凸显了入侵检测系统的重要性。针对朴素贝叶斯算法的缺陷,提出了一种改进后的朴素贝叶斯算法。该算法在原有的朴素贝叶斯模型基础上巧妙地引入属性加值算法,通过对分类参数的调控来实现简化分类数据复杂度的作用,并以计算出的最佳参数值来优化分类精确度。最后结合实验结果证明,在入侵检测框架中引入改进算法能够大幅度地降低入侵检测系统的误警率,从而提高系统的检测效率,减少网络攻击所带来的经济损失。

关键词: 朴素贝叶斯,入侵检测系统,属性加值,调控参数,误警率

Abstract: With increasing Internet connectivity and traffic volume,recent intrusion incidents have reemphasized the importance of network intrusion detection system (IDS)．According to the deficiency of the Naive Bayesian (NB) algorithm,this paper proposed an improved NB algorithm．This algorithm based on the original model is combined with a parameter of classification control.It can simplify the complexity of the classification of data and optimize the classification accuracy by computed parameter values．The experimental results prove that the algorithm used in the intrusion detection framework can drastically reduce the false alarm rate of IDS,thereby improve the detection efficiency and decrease economic damage brought by the cyber attack.

Key words: Naive Bayesian,IDS,Value attribute,Controlling parameter,False alarm rate

王辉,陈泓予,刘淑芬. 基于改进朴素贝叶斯算法的入侵检测系统[J]. 计算机科学, 2014, 41(4): 111-115. https://doi.org/

WANG Hui,CHEN Hong-yu and LIU Shu-fen. Intrusion Detection System Based on Improved Naive Bayesian Algorithm[J]. Computer Science, 2014, 41(4): 111-115. https://doi.org/

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