[1]王战红.特征和分类器参数组合优化的网络入侵检测[J].南京理工大学学报(自然科学版),2017,41(01):59.[doi:10.14177/j.cnki.32-1397n.2017.41.01.008]
 Wang Zhanhong.Network intrusion detection by using combination optimizingfeatures and classifier parameters[J].Journal of Nanjing University of Science and Technology,2017,41(01):59.[doi:10.14177/j.cnki.32-1397n.2017.41.01.008]
点击复制

特征和分类器参数组合优化的网络入侵检测()
分享到:

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
41卷
期数:
2017年01期
页码:
59
栏目:
出版日期:
2017-02-28

文章信息/Info

Title:
Network intrusion detection by using combination optimizingfeatures and classifier parameters
文章编号:
1005-9830(2017)01-0059-08
作者:
王战红
铁道警察学院 公安技术系,河南 郑州 450053
Author(s):
Wang Zhanhong
Department of Police Technology,Railway Police College,Zhengzhou 450053,China
关键词:
网络入侵 特征选择 分类器设计 生物地理学优化算法
Keywords:
network intrusion feature selection classifier design biogeography-based optimization algorithm
分类号:
TP393.08
DOI:
10.14177/j.cnki.32-1397n.2017.41.01.008
摘要:
为了提高网络入侵检测的入侵检测结果,该文设计了特征和分类器参数组合优化的网络入侵检测算法。分别分析了特征、分类器参数对入侵检测结果的影响,并建立了两者组合优化的数学模型,采用生物地理学优化算法模拟生物种群聚居栖息地的迁移过程对数学模型的最优解进行优化,找到最优的特征和分类器参数组合,最后采用标准入侵检测数据集—KDD Cup 99对算法的可行性和优越性进行测试和分析。结果表明,该文算法充分利用了特征和分类器参数之间的关联,改善了入侵检测率,执行速度可以满足入侵检测的实时性要求。
Abstract:
In order to obtain better intrusion detection results,this paper designs a network intrusion detection algorithm by using combination optimizing features and classifier parameters.A mathematical model of combinatorial optimization is set up based on the features and parameters of classifier influence on intrusion detection results respectively.A biogeography-based optimization algorithm is adopted to simulate migration process of species inhabitancy to find the optimal solution of mathematical model and obtain the optimal features and classifier parameters.Standard intrusion detection-KDD Cup 99 data sets are used to test feasibility and superiority.The results show that the proposed algorithm can make mine relation between features and classifier parameters to improve intrusion detection rate and that the execution speed can meet the real-time requirements of intrusion detection.

参考文献/References:

[1] 王飞,钱玉文,王执铨.基于无监督聚类算法的入侵检测[J].南京理工大学学报,2009,33(2):288-293.
Wang Fei,Qian Yuwen,Wang Zhiquan.Intrusion detection based on unsupervised clustering algorithm[J].Journal of Nanjing University of Science and Technology,2009,33(2):288-293.
[2]Denning D E.An intrusion detection model[J].IEEE Transactions on Software Engineering,2010,13(2):222-232.
[3]赵军.基于CEGA-SVM 的网络入侵检测算法[J].计算机工程,2009,35(23):166-167.
Zhao Jun.Network intrusion detection algorithm based on CEGA-SVM[J].Computer Engineering,2009,35(23):166-167.
[4]夏永祥,史意才.基于GPU和特征选择的SVM入侵检测模型[J].计算机工程,2012,38(8):111-113.
Xia Yongxiang,Shi Yicai.SVM intrusion detection model based on GPU and feature selection[J].Computer Engineering,2012,38(8):111-113.
[5]陈友,程学旗,李洋,等.基于特征选择的轻量级入侵检测系统[J].软件学报,2007,18(7):1639-1651.
Chen You,Cheng Xueqi,Li Yang,et al.Lightweight intrusion detection system based on feature selection[J].Journal of Software,2007,18(7):1639-1651.
[6]赵夫群.基于混合核函数的LSSVM网络入侵检测方法[J].现代电子技术,2015,38(21):96-99.
Zhao Fuqun.Detection method of LSSVM network intrusion based on hybrid kernel function[J].Modern Electronics Technique,2015,38(21):96-99.
[7]张宗飞.基于量子进化算法的网络入侵检测特征选择[J].计算机应用,2013,33(5):1357-1361.
Zhang Zongfei.Feature selection for network intrusion detection based on quantum evolutionary algorithm[J].Journal of Computer Applications,2013,33(5):1357-1361.
[8]井小沛,汪厚祥,聂凯,等.面向入侵检测的基于IMGA和MKSVM的特征选择算法[J].计算机科学,2012,39(7):96-101.
Jing Xiaopei,Wang Houxiang,Nie Kai,et al.Feature selection algorithm based on IMGA and MKSVM to intrusion detection[J].Computer Science,2012,39(7):96-101.
[9]Ding Zhiguo,Fei Minrui,Ma Haiping.Ensemble selection method based on biogeography-based optimization algorithm[J].Journal of System Simulation,2014,26(5):996-999.
[10]樊爱宛,时合生.基于特征选择和SVM参数同步优化的网络入侵检测[J].北京交通大学学报,2013,37(5):58-61.
Fan Aiwan,Shi Hesheng.Network intrusion detection based on simultaneous optimization of features selection and parameters of support vector machine[J].Journal of Beijing Jiaotong University,2013,37(5):58-61.
[11]向昌盛,张林峰.PSO-SVM在网络入侵检测中的应用[J].计算机工程与设计,2013,34(4):1222-1225.
Xiang Changshen,Zhang Linfeng.Application of support vector machine optimized by particle swarm optimization algorithm in network intrusion detection[J].Computer Engineering and Design,2013,34(4):1222-1225.
[12]朱红萍,巩青歌,雷战波.基于遗传算法的入侵检测特征选择[J].计算机应用研究,2012,29(4):1417-1419.
Zhu Hongping,Gong Qingge,Lei Zhanbo.Feature selection of intrusion detection based on genetic algorithm[J].Application Research of Computers,2012,29(4):1417-1419.
[13]张国辉,聂黎,张利平.生物地理学优化算法理论及其应用研究综述[J].计算机工程与应用,2015,51(3):12-17.
Zhang Guohui,Nie Li,Zhang Liping.Review on biogeography-based optimization algorithm and applications[J].Computer Engineering and Applications,2015,51(3):12-17.
[14]Panchal V,Singh P,Kaur N,Kundra H.Biogeography based satellite image classification[J].International Journal of Computer Science and Information Security,2009,6(2):269-274.

相似文献/References:

[1]袁家斌,浦海晨.基于遗传算法优化的神经网络电子邮件信息分类器的研究[J].南京理工大学学报(自然科学版),2008,(01):78.
 YUAN Jia-bin,PU Hai-chen.E-mail Information Classifier of Neural Network Based on Genetic Algorithm Optimization[J].Journal of Nanjing University of Science and Technology,2008,(01):78.
[2]赵海涛,金忠.一种改进的最佳鉴别平面[J].南京理工大学学报(自然科学版),2000,(01):88.
 ZhaoHaitao JinZhong.An Improved Optimal Discriminant Plane[J].Journal of Nanjing University of Science and Technology,2000,(01):88.
[3]黄 伟,陈 昊,郭雅娟,等.基于集成分类的恶意应用检测方法[J].南京理工大学学报(自然科学版),2016,40(01):35.
 Huang Wei,Chen Hao,Guo Yajuan,et al.Mobile malware detection approach using ensemble classification[J].Journal of Nanjing University of Science and Technology,2016,40(01):35.
[4]张前进,王华东.基于核典型相关分析和支持向量机的语音情感识别模型[J].南京理工大学学报(自然科学版),2017,41(02):191.[doi:10.14177/j.cnki.32-1397n.2017.41.02.009]
 Zhang Qianjin,Wang Huadong.Speech emotion recognition model based on kernel canonicalcorrelation analysis and support vector machine[J].Journal of Nanjing University of Science and Technology,2017,41(01):191.[doi:10.14177/j.cnki.32-1397n.2017.41.02.009]
[5]张佳欢,李磊军,李美争,等.基于变精度邻域粗糙集的多标记子空间研究[J].南京理工大学学报(自然科学版),2019,43(04):414.[doi:10.14177/j.cnki.32-1397n.2019.43.04.006]
 Zhang Jiahuan,Li Leijun,Li Meizheng,et al.Research on multi-label subspace based on variableprecision neighborhood rough sets[J].Journal of Nanjing University of Science and Technology,2019,43(01):414.[doi:10.14177/j.cnki.32-1397n.2019.43.04.006]
[6]陈 红,马盈仓,杨小飞,等.包含标签信息的最小二乘多标签特征选择算法[J].南京理工大学学报(自然科学版),2019,43(04):423.[doi:10.14177/j.cnki.32-1397n.2019.43.04.007]
 Chen Hong,Ma Yingcang,Yang Xiaofei,et al.Least squares multi-label feature selection algorithmwith label information[J].Journal of Nanjing University of Science and Technology,2019,43(01):423.[doi:10.14177/j.cnki.32-1397n.2019.43.04.007]

备注/Memo

备注/Memo:
收稿日期:2016-08-31 修回日期:2016-10-11
基金项目:河南省高等学校重点科研项目(15A120014); 公安部重点研究计划项目(201202ZDYJ017)
作者简介:王战红(1979-),男,讲师,主要研究方向:网络数据挖掘、信息网络安全,E-mail:wangzhanhong@rpc.edu.cn。
引文格式:王战红.特征和分类器参数组合优化的网络入侵检测[J].南京理工大学学报,2017,41(1):59-64.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2017-02-28