Feature selection algorithm based on improved particle swarm joint taboo search

Zhen ZHANG; Peng WEI; Yufeng LI; Julong LAN; Ping XU; Bo CHEN

doi:10.11959/j.issn.1000−436x.2018287

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Feature selection algorithm based on improved particle swarm joint taboo search

Papers | 更新时间：2024-06-05

- Feature selection algorithm based on improved particle swarm joint taboo search
- Journal on Communications Vol. 39, Issue 12, Pages: 60-68(2018)
- 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：
- 基金信息：
  
  The National Key Research and Development Program(No.2017YFB0803201);The National Natural Science Foundation of China(No.61502528);The Network Space Security Special Project(No.2017YFB0803204);The Shanghai Science and Technology Commission Research Project(No.16DZ1120503)
- DOI：10.11959/j.issn.1000−436x.2018287
  CLC： TP393
- Online First：2018-12，
  
  Published：25 December 2018
- 稿件说明：
移动端阅览
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：

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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