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计算机工程 ›› 2011, Vol. 37 ›› Issue (14): 103-105. doi: 10.3969/j.issn.1000-3428.2011.14.033

• 安全技术 • 上一篇    下一篇

基于GQPSO算法的网络入侵特征选择方法

牟 琦,毕孝儒,厍向阳   

  1. (西安科技大学计算机学院,西安 710054)
  • 收稿日期:2011-03-02 出版日期:2011-07-20 发布日期:2011-07-20
  • 作者简介:牟 琦(1974-),女,副教授,主研方向:网络安全,网络集成,数据库技术;毕孝儒,硕士研究生;厍向阳,副教授、博士后
  • 基金资助:
    陕西省自然科学基金资助项目(2009JM7007)

Feature Selection Method for Network Intrusion Based on GQPSO Algorithm

MU Qi, BI Xiao-ru, SHE Xiang-yang   

  1. (School of Computer, Xi’an University of Science and Technology, Xi’an 710054, China)
  • Received:2011-03-02 Online:2011-07-20 Published:2011-07-20

摘要: 高维网络数据中的无关属性和冗余属性容易使分类算法的网络入侵检测速度变慢、检测率降低。为此,提出一种基于遗传量子粒子群优化(GQPSO)算法的网络入侵特征选择方法,该方法将遗传算法中的选择变异策略与QPSO有机结合形成GQPSO算法,并以网络数据属性之间的归一化互信息量作为该算法适应度函数,指导其对网络数据的属性约简,实现网络入侵特征子集的优化选择。在KDDCUP1999数据集上进行仿真实验,结果表明,与QPSO算法、PSO算法相比,该方法能更有效地精简网络数据特征,提高分类算法的网络入侵检测速度及检测率。

关键词: GQPSO算法, 归一化互信息, 适应度函数, 特征选择, 网络入侵检测

Abstract: Aiming at problem that independent and redundant attributes of high dimensional network data cause classification algorithms’ slow detection speed and low detection rate in network intrusion detection, a feature selection approach for network intrusion based on Genetic Quantum Particle Swarm Optimization(GQPSO) algorithm is proposed. The approach organically combines selection and variation of genetic algorithm with QPSO to form GQPSO algorithm, and normalizes mutual information between attributes of network data is defined as the algorithm’s fitness function, which guides its reduction of network data attributes to realize optimal selection of network intrusion feature sub-set. Simulation experiment is done in KDDCUP1999. Result shows that compared with QPSO and PSO algorithms, the approach is more effective for feature selection of network data and improvement of network intrusion detection speed and detection rate of classification algorithms.

Key words: Genetic Quantum Particle Swarm Optimization(GQPSO) algorithm, normalized mutual information, fitness function, feature selection, network intrusion detection

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