粗糙集属性约简的极限学习机网络入侵检测算法
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摘要
针对网络入侵数据量大、属性冗余及属性之间线性相关导致分类算法计算速度慢、准确度不高等问题,提出一种改进粗糙集属性约简的极限学习机网络入侵分类算法。对训练集采用粗糙集正域和分辨矩阵相结合的方法获得属性核,筛选出只有属性核的数据集得到无冗余属性的特征集合;使用极限学习机(ELM)作为分类模型进行分类,使用支持向量机(SVM)、神经网络、极限学习机比较证明提出方法的有效性,为网络入侵检测提供一种新的解决方法。
Aiming at problem of slow computing speed and inaccuracy of the classification algorithm caused by large numbers of network intrusions,attribute redundancy and linear correlation between attributes,an extreme learning machine( ELM) network intrusion classification algorithm is proposed which is based on reduction of redundant attributes by the positive domain and discernibility matrixof the rough set. After the reduction of redundant attributes by the positive domain and discernibility matrix of the rough set,it gets the characteristic collection of non-redundant attributes. ELM serves as classification model. Through comparing it with SVM,neural network,ELM,the effectiveness of this method is proved,thus provide a new solution for network intrusion detection.
Aiming at problem of slow computing speed and inaccuracy of the classification algorithm caused by large numbers of network intrusions,attribute redundancy and linear correlation between attributes,an extreme learning machine( ELM) network intrusion classification algorithm is proposed which is based on reduction of redundant attributes by the positive domain and discernibility matrixof the rough set. After the reduction of redundant attributes by the positive domain and discernibility matrix of the rough set,it gets the characteristic collection of non-redundant attributes. ELM serves as classification model. Through comparing it with SVM,neural network,ELM,the effectiveness of this method is proved,thus provide a new solution for network intrusion detection.
引文
[1]刘宏立,李璐,胡久松.基于CS-CPSO与SVM融合的WSNs入侵检测算法[J].传感器与微系统,2017,36(9):110-112.
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[6]马世欢,胡彬.基于特征选取和样本选择的网络入侵检测[J].计算机系统应用,2015,24(9):240-243.
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[8] Huang G B,Saratchandran P,Sundararajan N. An efficient sequential learning algorithm for growing and pinning RBF(GAPRBF)networks[J]. IEEEE Transactions on Systems,Man and Cybemetics,Part B(Cybernetics),2004,34(6):2284-292.
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[11] Mirza B,Lin Z,Toh K A. Weighted online sequential extreme learning machine for class imbalance learning[J]. Neural Processing Letters,2013,38(3):465-486.
[12]宋宇翔,刘琰.特征和分类器联合优化的网络入侵检测算法[J].计算机工程应用,2012,48(19):77-81.
[13] Modi C,Patel D,Borisaniya B,et al. A survey of intrusion detection techniques in cloud[J]. Journal of Network and Computer Applications,2013(1):3-5.
[14]杨富华,彭钢. PCA-SVM在网络入侵检测中的仿真研究[J].计算机仿真,2011,28(7):146-149.
[15]康松林,刘楚楚,樊晓平,等. WOS-ELM算法在入侵检测中的研究[J].小型微型计算机系统,2015,8(8):1779-1783.
[16] Huang G B,Zhou H M,Ding X J. Extreme learning machine for regression and multiclass classification[J]. IEEE Transactions on Systems Man and Cybernetics,2012,42(2):513-529.
[17]樊持杰,陈丽敏,夏春艳.基于粗糙集的属性约算法[J].微计算机信息,2010,26(6):222-223,228.
[18]李丛,闫仁武,朱长水,等.融合Fast特征选择与ABQGSASVM的网络入侵检测[J].计算机应用研究,2017,33(07):1-10.
[19]李玉霞,刘丽,沈桂兰.基于AFSA-SVM的网络入侵检测模型[J].计算机工程应用,2013,49(24):74-77.
[2]王文莉,侯丽敏.基于邻域粗糙集的入侵检测[J].传感器与微系统,2010,29(6):36-38,41.
[3]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].计算机学,2012,39(7):96-99,111.
[4]沈夏炯,王龙,韩道军.人工蜂群优化的BP神经网络在入侵检测中的应用[J].计算机工程,2016(2):190-194.
[5] Chitrakar R,Huang C H. Selection of candidate support vectors in increment SVM for network intrusion detection[J]. Computers&Security,2014,45(0167-4048):231-234.
[6]马世欢,胡彬.基于特征选取和样本选择的网络入侵检测[J].计算机系统应用,2015,24(9):240-243.
[7] Huang G B,Zhu Q Y,Siew Chee-Kheong. Extreme learning machine:Theory and applications[J]. Neurocomputing,2006,70(1):489-501.
[8] Huang G B,Saratchandran P,Sundararajan N. An efficient sequential learning algorithm for growing and pinning RBF(GAPRBF)networks[J]. IEEEE Transactions on Systems,Man and Cybemetics,Part B(Cybernetics),2004,34(6):2284-292.
[9]谭爱平,陈浩,吴伯桥.基于SVM的网络入侵检测集成学习算法[J].计算机学,2014(2):197-200.
[10]刘明珍.粒子群优化支持向量机的入侵检测算法[J].计算机工程与应用,2012,(35):71-74,105.
[11] Mirza B,Lin Z,Toh K A. Weighted online sequential extreme learning machine for class imbalance learning[J]. Neural Processing Letters,2013,38(3):465-486.
[12]宋宇翔,刘琰.特征和分类器联合优化的网络入侵检测算法[J].计算机工程应用,2012,48(19):77-81.
[13] Modi C,Patel D,Borisaniya B,et al. A survey of intrusion detection techniques in cloud[J]. Journal of Network and Computer Applications,2013(1):3-5.
[14]杨富华,彭钢. PCA-SVM在网络入侵检测中的仿真研究[J].计算机仿真,2011,28(7):146-149.
[15]康松林,刘楚楚,樊晓平,等. WOS-ELM算法在入侵检测中的研究[J].小型微型计算机系统,2015,8(8):1779-1783.
[16] Huang G B,Zhou H M,Ding X J. Extreme learning machine for regression and multiclass classification[J]. IEEE Transactions on Systems Man and Cybernetics,2012,42(2):513-529.
[17]樊持杰,陈丽敏,夏春艳.基于粗糙集的属性约算法[J].微计算机信息,2010,26(6):222-223,228.
[18]李丛,闫仁武,朱长水,等.融合Fast特征选择与ABQGSASVM的网络入侵检测[J].计算机应用研究,2017,33(07):1-10.
[19]李玉霞,刘丽,沈桂兰.基于AFSA-SVM的网络入侵检测模型[J].计算机工程应用,2013,49(24):74-77.