改进粒子群联合禁忌搜索的特征选择算法

张震; 魏鹏; 李玉峰; 兰巨龙; 徐萍; 陈博

doi:10.11959/j.issn.1000−436x.2018287

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改进粒子群联合禁忌搜索的特征选择算法

学术论文 | 更新时间：2024-06-05

- 改进粒子群联合禁忌搜索的特征选择算法
- Feature selection algorithm based on improved particle swarm joint taboo search
- 通信学报 2018年39卷第12期页码：60-68
- 作者机构：
  
  1. 国家数字交换系统工程技术研究中心，河南郑州 450002
  2. 中国人民解放军战略支援部队信息工程大学，河南郑州 450002
- 作者简介：
  
  [ "张震（1985−），男，山东济宁人，博士，国家数字交换系统工程技术研究中心讲师，主要研究方向为网络测量、网络管理。" ]
  [ "魏鹏（1994−），男，湖南衡阳人，国家数字交换系统工程技术研究中心硕士生，主要研究方向为新型网络体系结构。" ]
  [ "李玉峰（1976−），男，山东烟台人，博士，国家数字交换系统工程技术研究中心副教授，主要研究方向为宽带信息网络、高速路由器核心技术。" ]
  [ "兰巨龙（1962−），男，河北张北人，博士，国家数字交换系统工程技术研究中心教授、博士生导师，主要研究方向为宽带信息网络。" ]
  [ "徐萍（1983−），女，江西永新人，中国人民解放军战略支援部队信息工程大学讲师，主要研究方向为信息素质教育、信息资源建设。" ]
  [ "陈博（1989−），男，河南商丘人，国家数字交换系统工程技术研究中心博士生、讲师，主要研究方向为网络安全。" ]
- 基金信息：
  
  国家重点研究发展计划基金资助项目(No.2017YFB0803201);国家自然科学基金资助项目(No.61502528);网络空间安全专项课题基金资助项目(No.2017YFB0803204);上海市科学技术委员会科研计划课题基金资助项目(No.16DZ1120503)
- DOI：10.11959/j.issn.1000−436x.2018287
  中图分类号： TP393
- 网络首发：2018-12，
  
  纸质出版：2018-12-25
- 稿件说明：
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张震, 魏鹏, 李玉峰, 等. 改进粒子群联合禁忌搜索的特征选择算法[J]. 通信学报, 2018,39(12):60-68.

Zhen ZHANG, Peng WEI, Yufeng LI, et al. Feature selection algorithm based on improved particle swarm joint taboo search[J]. Journal on Communications, 2018, 39(12): 60-68.
张震, 魏鹏, 李玉峰, 等. 改进粒子群联合禁忌搜索的特征选择算法[J]. 通信学报, 2018,39(12):60-68. DOI： 10.11959/j.issn.1000−436x.2018287.

Zhen ZHANG, Peng WEI, Yufeng LI, et al. Feature selection algorithm based on improved particle swarm joint taboo search[J]. Journal on Communications, 2018, 39(12): 60-68. DOI： 10.11959/j.issn.1000−436x.2018287.

摘要

针对入侵检测中数据特征维度高的问题，提出了改进粒子群联合禁忌搜索(IPSO-TS)的特征选择算法。采用遗传算子对粒子群算法进行了改进，得到了特征选择初始最优解；对该解进行禁忌搜索(TS)得到了特征子集的全局优化解。基于KDD CUP 99数据集的实验结果表明，相较遗传算子整合粒子群算法(CMPSO)、粒子群算法(PSO)和粒子群联合禁忌算法，IPSO-TS减少了至少29.2%的特征，缩短了至少15%的平均检测时间，提高了至少2.96%的平均分类准确率。

Abstract

To solve the problem of high data feature dimensionality in intrusion detection

a feature selection algorithm based on improved particle swarm optimization taboo search (IPSO-TS) was proposed. The genetic algorithm was used to improve the particle swarm optimization

and the initial optimal solution of feature selection was obtained. A taboo search (TS) algorithm was used for initial optimal solution to obtain the global optimal solution of the feature subset. Compared with genetic algorithm integrated particle swarm optimization (CMPSO)

particle swarm optimization (PSO) and PSO-TS algorithms

experimental results based on the KDD CUP 99 dataset show that the method reduces the features by about 29.2%

shortens about 15% of the average detection time

and increases about 2.96% of the average classification accuracy.

关键词

Keywords

references

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