计算机工程与应用 ›› 2010, Vol. 46 ›› Issue (21): 94-96.DOI: 10.3778/j.issn.1002-8331.2010.21.026

• 网络、通信、安全 • 上一篇    下一篇

改进的AdaBoost算法在IDS入侵检测中的应用

陈 念1,王汝传2   

  1. 1.池州学院 计算机科学系,安徽 池州 247000
    2.南京邮电大学 计算机科学系,南京 210093
  • 收稿日期:2010-02-25 修回日期:2010-05-13 出版日期:2010-07-21 发布日期:2010-07-21
  • 通讯作者: 陈 念

Detection of intrusion samples in IDS based on improved AdaBoost algorithm

CHEN Nian1,WANG Ru-chuan2   

  1. 1.Department of Computer Science,Chizhou College,Chizhou,Anhui 247000,China
    2.Department of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210093,China
  • Received:2010-02-25 Revised:2010-05-13 Online:2010-07-21 Published:2010-07-21
  • Contact: CHEN Nian

摘要: 网络入侵检测系统IDS中,异常数据所占的比例非常小,属于小类样本,却是检测的目标。在AdaBoost算法基础上进行改进,通过对大类样本权重设置阈值,对权值超过阈值的样本进行相应处理,来削弱分类器对大类样本错分的重视程度,减轻下一级训练的负担,从而有效地强化对小类错分样本的学习,提高入侵检测的精度,降低误报率和漏报率。方法在KDD-99数据集上进行实验,并与SVM方法检测结果进行比较,取得了很好的效果。

关键词: 阈值, AdaBoost算法, 入侵检测系统, 分类器

Abstract: In network intrusion detection system,abnormal samples are the goal of detection,although the proportion of them is very small.Based on AdaBoost algorithm,this paper sets threshold value to the samples of majority class and processes those whose weight is above the threshold.The improved algorithm can effectively reduce the burden of classifier on next level of training and strengthen the learning to the samples of minority class.The results experimented on KDD-99 sets and comparison with SVM method show the algorithm effectively reduces the false alarm rate and omission rate.

Key words: threshold, AdaBoost algorithm, intrusion detection system, classifier

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