• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2010, Vol. 32 ›› Issue (6): 37-39.doi: 10.3969/j.issn.1007130X.2010.

• 论文 • 上一篇    下一篇

基于神经网络和CFS特征选择的网络入侵检测系统

孙宁青   

  1. (广西工业职业技术学院,广西 南宁 530003)
  • 收稿日期:2009-09-12 修回日期:2009-12-10 出版日期:2010-06-01 发布日期:2010-06-01
  • 通讯作者: 孙宁青 E-mail:superzhoujunpeng@163.com
  • 作者简介:孙宁青(1963),男,广西南宁人,副教授,研究方向为计算机应用技术。

Based on Neural Networks and the CFSBased Feature Selection

SUN Ningqing   

  1. (Guangxi Vocational and  Technical Institute of Industry,Nanning 530003,China)
  • Received:2009-09-12 Revised:2009-12-10 Online:2010-06-01 Published:2010-06-01

摘要:

本文提出了一种新型的基于CFS特征选择和神经网络的高效入侵检测模型。通过使用该模型对经过特征提取后的攻击数据的训练学习,可以有效地识别各种入侵。在经典的KDD Cup 1999入侵检测数据集上的测试说明,该模型能够高效地对攻击模式进行训练学习,从而正确有效地检测网络攻击。

关键词: 入侵检测, 特征选择, 神经网络, CFS

Abstract:

This paper introduces a novel intrusion detection model based on neural networks and the CFS (correlationbased feature selection) based feature selection mechanism. It can effectively detect several types of attacks by combining neural networks and the CFSbased feature selection. The experiments upon the wellknown KDD Cup 1999 intrusion detection dataset demonstrate that the model is actually effective in practice.

Key words: intrusion detection;feature selection;neural network;correlationbased feature selection

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