• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学

• 计算机网络与信息安全 • 上一篇    下一篇

基于NBSR模型的入侵检测技术

朱世松,巴梦龙,王辉,申自浩   

  1. (河南理工大学计算机科学与技术学院,河南 焦作 454000)
  • 收稿日期:2019-09-02 修回日期:2019-10-23 出版日期:2020-03-25 发布日期:2020-03-25
  • 基金资助:

    国家自然科学基金(61300216)

An intrusion detection technology based on NBSR model

ZHU Shi-song,BA Meng-long,WANG Hui,SHEN Zi-hao   

  1. (School of Computer Science and Techology,Henan Polytechnic University,Jiaozuo 454000,China)

     
  • Received:2019-09-02 Revised:2019-10-23 Online:2020-03-25 Published:2020-03-25

摘要:

为了更好地解决入侵检测技术中误用检测造成未知入侵行为误检率升高的问题,提出了一种基于NBSR模型的入侵检测技术。首先,为了弥补ReliefF特征选择算法对特征之间的相关性分析的不足,引入Pearson相关系数,提出Relieff-P算法。其次,利用Relieff-P算法对UNSW-NB15数据集进行处理,去除无关特征,得到新的特征子集。最后,将朴素贝叶斯分类器和Softmax回归分类器级联构成NBSR分类器,建立了NBSR模型。在UNSW-NB15测试集上的实验结果表明,NBSR模型较其他检测模型有较低的误检率。
 

关键词: 朴素贝叶斯, Softmax回归, 入侵检测系统, 误检率

Abstract:

In order to better solve the problem of increasing the false positive rate of unknown intrusion behaviors by misuse detection in intrusion detection technology, an intrusion detection technology based on NBSR model is proposed. Firstly, in order to compensate for the lack of correlation analysis between features by the ReliefF feature selection algorithm, the Pearson correlation coefficient is introduced and the Relieff-P algorithm is proposed. Secondly, the Relieff-P algorithm is used to process the UNSW-NB15 dataset to remove irrelevant features and obtain a new feature subset. Finally, the naive Bayes classifier and the Softmax regression classifier are cascaded to form the NBSR classifier, and NBSR model was established. The experimental results on the UNSW-NB15 test set show that the NBSR model has lower false positive rate than other detection models.

 

Key words: naive Bayes, Softmax regression, intrusion detection system, false positive rate