检索
E-mail
RSS
作者投稿和查稿 主编审稿 专家审稿 编委审稿 远程编辑

计算机工程 ›› 2010, Vol. 36 ›› Issue (06): 262-264.doi: 10.3969/j.issn.1000-3428.2010.06.089

• 开发研究与设计技术 • 上一篇    下一篇

入侵检测中的快速特征选择方法

郑洪英，侯梅菊，王 渝   

  1. (重庆大学计算机科学与工程学院，重庆 400030)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2010-03-20 发布日期:2010-03-20

Fast Method for Feature Selection in Intrusion Detection

ZHENG Hong-ying, HOU Mei-ju, WANG Yu   

  1. (School of Computer Science and Engineering, Chongqing University, Chongqing 400030)
  • Received:1900-01-01 Revised:1900-01-01 Online:2010-03-20 Published:2010-03-20

PDF

1231

被引次数

36 | 11

摘要： 进行入侵检测前必须分析输入数据的特征。使用粒子群优化算法对特征进行选择，消除冗余属性、降低问题规模、提高数据分类质量、加快数据处理速度。用二进制字符串序列表示粒子位置，阐述位置和速度的更新策略以及适应度函数的选择。在KDD CUP1999数据集上进行实验，结果表明与遗传进化算法相比，该方法可以更有效地精简特征，提高分类质量。

关键词: 粒子群优化算法, 特征选择, 入侵检测, 最优化

Abstract: It is necessary to analyze feature of input data before intrusion detection. This paper uses Particle Swarm Optimization(PSO) algorithm to select feature, eliminate the redundancy property, reduce the problem size, improve the quality of data classification and speed up the process. The position of the particle is expressed in a binary string. The update strategies of the position, velocity and the selection of fitness function are illustrated. The experiments with KDD CUP1999 and comparative results with Genetic Algorithm(GA) are described. It shows that the method is more efficient for feature selection and classification quality improvement.

Key words: Particle Swarm Optimization(PSO) algorithm, feature selection, intrusion detection, optimization

中图分类号: