计算智能分类方法及其在入侵检测中的应用研究
|
摘要
分类，顾名思义是将无规律的事物分为有规律，它是当今信息处理、数据挖掘和知识发现等诸多领域中的一项重要任务。随着信息技术的迅猛发展以及信息量呈指数形式增长，常用的分类方法凸显出不足，而智能分类法得到广泛应用和重视，特别是计算智能分类方法的研究具有重要的理论意义和应用价值。
入侵检测是对入侵行为的检测，主要区分正常网络行为和异常入侵行为及其类型，在实际检测中是一个多分类问题，而采用计算智能分类法无疑能够大大提高入侵检测的效果。为此，本论文针对计算智能分类方法及其在入侵检测中的应用进行研究，其主要工作或创新如下：
（1）为了解决粒子群（PSO）算法存在过早收敛、陷入局部极小等问题，研究了基于云模型的粒子群（CPSO）算法，主要采用云模型动态确定惯性权重，可以取得较快的优化速度且能避免陷入局部极小，经经典优化函数测试，结果表明CPSO算法优于PSO算法和蚁群（ACO）算法。进而研究了基于CPSO的神经网络分类方法，可以克服神经网络分类精度较低的缺点。仿真实验表明其分类方法在分类精度上得到较大提高。
（2）基于统计学习理论的支持向量机（SVM）在分类上具有独特的优势，为了解决支持向量机（SVM）模型中惩罚参数和核参数凭经验选取或试验法的选取问题，运用云模型能提高优化策略、加快收敛速度等优点，研究基于云PSO的SVM分类方法（CPSO-SVM），即采用云PSO算法优化SVM模型及其参数。实验表明CPSO-SVM分类方法在入侵检测中，其检测精度高于经典SVM和基于PSO的SVM方法（PSO-SVM）。
（3）基于稀疏贝叶斯框架下的相关向量机（RVM）具有计算量少、分类精度高等优点，但也存在模型参数的优化问题。为此，研究了基于云PSO的RVM分类方法（CPSO-RVM），即采用云PSO算法优化RVM模型及其核函数宽度参数。通过典型实验和KDDCup99数据库入侵检测资料的多分类问题的实际检测，结果表明：与PSO-RVM、PSO-SVM和CPSO-SVM等多种分类方法相比，CPSO-RVM分类方法的检测精度最高，大大降低了误报率。这为CPSO-RVM的广泛应用提供了科学依据。
Classification, as the term implies in itself, is the process or method of makingirregular things regular. With the rapid development of information technology and theenormous increase of its amount in the form of index numbers, classification has becomean important task in such fields as information processing, data mining and knowledgediscovering. However, the traditional classification methods have demonstrated manyweaknesses and at the same time, intelligence classification method, especiallycomputational intelligence classification method, has drawn special attention and beenwidely used. In this sense, studies in this field have acquired both theoretical significanceand practical value.
Intrusion detection, a detection of intrusive action which mainly distinguishes betweennormal network activities and abnormal intrusive ones, is a multi-classification problem inreality. Given that the adoption of computational intelligence classification method canundoubtedly strengthen the effect of intrusion detection, computational intelligenceclassification methods, as well as their application in intrusion detection, are mainlydiscussed and studied in this thesis. Related work and innovations are as follows:
(1) To solve such problems as premature convergence and an easy falling into localminima in PSO, CPSO based on cloud model is proposed. The dynamic examination ofinertia weight by adopting cloud model can get a faster optimization speed, avoiding aneasy falling into local minima. After a classical optimization function test, it is concludedthat CPSO is superior to PSO and ACO. Then a neural network classification method basedon CPSO, which can overcome limitations of low accuracy in the neural network algorithm,is also put forward in the thesis. A simulated experiment has shown that this method hasbeen greatly improved concerning classification accuracy.
(2) SVM based on statistical learning theories possesses unique advantages inclassification. To solve the limit of experience-based penalty parameter C and kernelparameter in SVM, a cloud model, which can optimize the relevant strategies and increaseconvergence rate, is adopted. The CPSO-SVM, the optimization of SVM model and itsparameter by adopting cloud PSO, is studied. Experiments suggest that in intrusion detection, the accuracy of CPSO-SVM is higher than the classical SVM and PSO-SVM.
(3) RVM based on sparse Bayesian framework possesses such advantages as lesscalculation and higher classification accuracy and on the other hand such a disadvantage ofunoptimized parameter. As a result, CPSO-RVM, the optimization of RVM model and itskernel function parameters by adopting cloud PSO, is also studied. Through classicalexperiments and actual detection of multi-classification problems in intrusion detectiondatum of KDDCup99database, it is suggested that compared with such classificationmethods as PSO-RVM, PSO-SVM and CPSO-SVM, CPSO-RVM, CPSO-RVM has thehighest detection accuracy and greatly reduced the rate of misinforming, thus offering ascientific basis for the extensive application of CPSO-RVM.
入侵检测是对入侵行为的检测，主要区分正常网络行为和异常入侵行为及其类型，在实际检测中是一个多分类问题，而采用计算智能分类法无疑能够大大提高入侵检测的效果。为此，本论文针对计算智能分类方法及其在入侵检测中的应用进行研究，其主要工作或创新如下：
（1）为了解决粒子群（PSO）算法存在过早收敛、陷入局部极小等问题，研究了基于云模型的粒子群（CPSO）算法，主要采用云模型动态确定惯性权重，可以取得较快的优化速度且能避免陷入局部极小，经经典优化函数测试，结果表明CPSO算法优于PSO算法和蚁群（ACO）算法。进而研究了基于CPSO的神经网络分类方法，可以克服神经网络分类精度较低的缺点。仿真实验表明其分类方法在分类精度上得到较大提高。
（2）基于统计学习理论的支持向量机（SVM）在分类上具有独特的优势，为了解决支持向量机（SVM）模型中惩罚参数和核参数凭经验选取或试验法的选取问题，运用云模型能提高优化策略、加快收敛速度等优点，研究基于云PSO的SVM分类方法（CPSO-SVM），即采用云PSO算法优化SVM模型及其参数。实验表明CPSO-SVM分类方法在入侵检测中，其检测精度高于经典SVM和基于PSO的SVM方法（PSO-SVM）。
（3）基于稀疏贝叶斯框架下的相关向量机（RVM）具有计算量少、分类精度高等优点，但也存在模型参数的优化问题。为此，研究了基于云PSO的RVM分类方法（CPSO-RVM），即采用云PSO算法优化RVM模型及其核函数宽度参数。通过典型实验和KDDCup99数据库入侵检测资料的多分类问题的实际检测，结果表明：与PSO-RVM、PSO-SVM和CPSO-SVM等多种分类方法相比，CPSO-RVM分类方法的检测精度最高，大大降低了误报率。这为CPSO-RVM的广泛应用提供了科学依据。
Classification, as the term implies in itself, is the process or method of makingirregular things regular. With the rapid development of information technology and theenormous increase of its amount in the form of index numbers, classification has becomean important task in such fields as information processing, data mining and knowledgediscovering. However, the traditional classification methods have demonstrated manyweaknesses and at the same time, intelligence classification method, especiallycomputational intelligence classification method, has drawn special attention and beenwidely used. In this sense, studies in this field have acquired both theoretical significanceand practical value.
Intrusion detection, a detection of intrusive action which mainly distinguishes betweennormal network activities and abnormal intrusive ones, is a multi-classification problem inreality. Given that the adoption of computational intelligence classification method canundoubtedly strengthen the effect of intrusion detection, computational intelligenceclassification methods, as well as their application in intrusion detection, are mainlydiscussed and studied in this thesis. Related work and innovations are as follows:
(1) To solve such problems as premature convergence and an easy falling into localminima in PSO, CPSO based on cloud model is proposed. The dynamic examination ofinertia weight by adopting cloud model can get a faster optimization speed, avoiding aneasy falling into local minima. After a classical optimization function test, it is concludedthat CPSO is superior to PSO and ACO. Then a neural network classification method basedon CPSO, which can overcome limitations of low accuracy in the neural network algorithm,is also put forward in the thesis. A simulated experiment has shown that this method hasbeen greatly improved concerning classification accuracy.
(2) SVM based on statistical learning theories possesses unique advantages inclassification. To solve the limit of experience-based penalty parameter C and kernelparameter in SVM, a cloud model, which can optimize the relevant strategies and increaseconvergence rate, is adopted. The CPSO-SVM, the optimization of SVM model and itsparameter by adopting cloud PSO, is studied. Experiments suggest that in intrusion detection, the accuracy of CPSO-SVM is higher than the classical SVM and PSO-SVM.
(3) RVM based on sparse Bayesian framework possesses such advantages as lesscalculation and higher classification accuracy and on the other hand such a disadvantage ofunoptimized parameter. As a result, CPSO-RVM, the optimization of RVM model and itskernel function parameters by adopting cloud PSO, is also studied. Through classicalexperiments and actual detection of multi-classification problems in intrusion detectiondatum of KDDCup99database, it is suggested that compared with such classificationmethods as PSO-RVM, PSO-SVM and CPSO-SVM, CPSO-RVM, CPSO-RVM has thehighest detection accuracy and greatly reduced the rate of misinforming, thus offering ascientific basis for the extensive application of CPSO-RVM.
引文
[1]叶莲.基于免疫算法的分类方法及其应用研究[D].重庆:重庆大学博士学位论文,2012
[2]史忠植.知识发现[M].北京:清华大学出版社,2002
[3]张乃尧,阎平凡.神经网络与模糊控制[M].北京:清华大学出版社,1999
[4]焦李成,慕彩红,王伶.通信中的智能信号处理[M].北京:电子工业出版社,2006
[5]白鹏,张喜斌,张斌等.支持向量机理论及工程应用实例[M].西安:西安电子科技大学出版社,2007
[6] Tipping M E, Faul A C. Fast Marginal Likelihood Maximization for Sparse Bayesian Models[A].KeyWest, FL: In CM Bishop, BJ Frey (Eds.), Proceedings of the Ninth International Workshop on ArtificialIntelligence and Statistics[C],2003,3-6
[7] Tipping M E. Sparse Bayesian Learning and the Relevance Vector Machine [J].Journal of machinelearning research.2001,1(3):211-244.
[8]董争.相关向量机学习的研究[J].技术与市场,2009,16(4):39-40
[9]杨树仁,沈宏远.基于相关向量机的机器学习算法研究与应用[J].计算技术与自动化,2010,29(1):43-47
[10]杨国鹏,周欣,余旭初.稀疏贝叶斯模型与相关向量机学习研究[J].计算机科学,2010,37(7):225-228
[11] Lee W, Xiang D. Information-Theoretic Measures for Anomaly Detection[A].In Proceedings of the2001IEEE Symposium on Security and Privacy.May[C],2001,130-143.
[12] Lane T. Machine learning techniques for the computer security domain of anomaly detection[D].Purdue University,2000.
[13] Matthew G, et al. Data mining methods for detection of new malicious executables[A]. Proceedingsof IEEE Symposium on Security and Privacy(IEEE S&P-2001)[C], Oakland,CA: May2001.14-17.
[14]郑成兴.网络入侵防范的理论与实践[M].北京:国防工业出版社,2006.
[15]龙冬阳.网络安全技术及应用[M].广州:华南理工大学出版社,2006.
[16]李剑.入侵检测技术[M].北京:高等教育出版社,2008.
[17]杨智君,田地,马骏骁.入侵检测技术研究综述[J].计算机工程与设计,2006,27(12):2119-2123
[18]史天运,贾利民.计算智能理论及其在RITS中的应用[J].交通运输系统工程与信息,2002,2(1):10-15.
[19]徐宗本,张讲社,郑亚林.计算智能中的仿生学:理论与算法[M].北京:科学出版社,2003.
[20]李澄非.计算智能方法研究及其在流程工业中应用[D].北京:北京化工大学博士学位论文,2007.
[21] David Shilane, Jarno Martikainen, Sandrine Dudoit, et al. A general framework for statisticalperformance comparison of evolutionary computation algorithms[J]. Information Sciences,2008,178(14-15):2870-2879.
[22]余有明,刘玉树.进化计算的理论与算法[J].计算机应用研究.2005,22(9):77-80.
[23]彭祖赠,孙韫玉.模糊(Fuzzy)数学及其应用[M].武汉:武汉大学出版社,2007.
[24] Amit Kumar Banerjee, K. Kiran, U.S.N. Murty, Ch. Venkateswarlu. Classification and identificationof mosquito species using artificial neural networks[J]. Computational Biology and Chemistry,2008,32(6):442-447.
[25]阎平凡,张长水,人工神经网络与模拟进化计算[M].北京:清华大学出版社,2000.
[26]丁永生.计算智能理论、技术与应用[M].北京:科学出版社,2004.
[27] Bart Baesens, Rudy Setiono, Christophe Meues, et al. Using Neural Network Rule Extraction andDecision Tables for Credit-Risk Evaluation[J]. Management Science,2003,49(3):312-329.
[28]王凌,智能优化算法及其应用[M].北京:清华大学出版社,2001.
[29]江沛.智能优化技术的研究及其在化工生产中的应用[D].北京:北京化工大学硕士学位论文,2008.
[30]罗熊,孙增圻.计算智能方法优化设计模糊控制系统:现状与展望[J].控制与决策,2007,22(9):961-966.
[31]盖斐,赵建玉,贾磊.基于智能计算的干线多路口交通信号控制[J].济南大学学报(自然科学版),2008,22(2):137-144.
[32]苏建元.计算智能主要算法的比较与融合[J].中国电子科学研究院学报,2007,2(1):52-56.
[33]马利.基于数据挖掘的聚类分析和传统聚类分析的对比研究[J].数理医药学杂志,2008,21(5):530-531
[34]黄利文.数据挖掘中的聚类分析方法[J].电脑知识与技术,2008,12(2):564-566
[35]魏晓云.决策树分类方法研究[J].计算机系统应用,2007,16(9):42-45
[36]魏红宁.基于SPRINT方法的并行决策树分类研究[J].计算机应用,2005,25(1):39-41
[37]段青.基于稀疏贝叶斯学习方法的回归与分类在电力系统中的预测研究[D].济南:山东大学博士学位论文,2010
[38]冀俊忠,刘椿年,阎静.一种快速的贝叶斯网结构学习算法[J].计算机研究与发展,2007,44(3):412-419
[39]范劲松,方廷建.基于粗集理论和SVM算法的模式分类算法[J].模式识别和人工智能,2000,13(4):419-423
[40] Andrew R.J. Clark, Richard M. Everson. Multi-objective learning of Relevance Vector Machineclassifiers with multi-resolution kernels[J].Pattern Recognition,2012,45(9):3535-3543
[41] Clodoaldo A.M. Lima, AndréL.V. Coelho, Sandro Chagas. Automatic EEG signal classification forepilepsy diagnosis with Relevance Vector Machines[J]. Expert Systems with Applications,2009,36(6):10054-10059.
[42] Xiaodong Wang, Meiying Ye, C.J. Duanmu. Classification of data from electronic nose usingrelevance vector machines[J]. Sensors and Actuators B: Chemical,2009,140(1):143-148
[43] MCCULLOCH W S, PITTS W H. A logical calculus of the ideas immanent in neuron activity[J].Bulletin Mathematical Biophysics,1943:115-133.
[44] WIDROW B. An adaptive adaline neuron using chemical memistors[M]. Standford ElectronicsLavoratroy Technical Report,1960.
[45] RUMELLHART D E, MCCLLELAND J L.Parallel distributed processing [M]. Cambridge, MA:MIT Press.1986.
[46] SAITO K, NAKANO R. Medical diagnostic expert system based on PDP mode [J].IEEEinternational conf.on Neural Networks,1988(1):225-262.
[47]王刚,黄丽华,张成洪,夏洁.数据挖掘分类算法研究综述[J].科技导报,2006,24(12):73-76
[48]葛黎辉.支持向量机及其金融应用[D].南京:南京农业大学大学硕士学位论文,2009.
[49]杨斌,匡立春,孙中春,等.一种用于测井油气层综合识别的支持向量机方法[J].测井技术,2005,29(12):511-514
[50]邓乃扬.支持向量机——理论、算法与拓展[M].北京:科学出版社,2009
[51] Osuna E.Freund,R.Cirusi,etc. Training algorithm for support vector machines[A].In:7th IEEEworkshop on Neural Networks for Signal Proeessing[C],1997,276-285.
[52] Joaehims,T.Making Large-Scale SVM Leaming Practical[J].Cambridge,MA:MIT press,1995,169-154.
[53] J.platt.Fast training of support vector machines using sequential minimal optimization[J].Cambridge,MA:MIT Press,1999,185-208.
[54] Lin C J.Asymptotic convergence of an SMO algorithm without any assumptions[A].IEEETrans.Neural News[C].2002.
[55] S.S.Keerthi, etc. Improvements to platt’s SMO Algorithm for SVM Classifier Design[J]. MIT PressJournals,2001,13(3):637-649.
[56] S.S.Keerthi, etc. Convergence of a Generalized SMO Algorithm for SVM Classifier Design[J].Machine Learning,2002,46(1-3):351-360.
[57] J-X.Dong,A.Krzyzak,and C.Y.Suen. Fast SVM Training Algorithm with Decomposition on VeryLarge Data sets[J]. IEEE Trans.Pattern Analysis and Machine Intelligence,2005,27(4),603-618.
[58] J.Cervantes etc.Support Vector Machine Classification Based on Fuzzy Clustering for LargeDatasets[J].MICAI2006:Advances in Artificial Intelligence,Srpinger-Verlgag,2006,4293,572-582.
[59] XiaoOu Li, J Cervantes, and Wen Yu.Two-stage SVM classification for large datasets via randomlyreducing and recovering training data[A]. IEEE International Conference, Systems, Man andCybernetics[C],2007,3633-3638.
[60] Zheng Rong Yang. A Fast Algorithm for Relevance Vector Machine[A].7th International Conferenceon Intelligent Data Engineering and Automated Learning, IDEAL2006, IEEE press[C],2006,33-39
[61] Catarina Sliva, Bernardete Ribeiro. Towards expanding relevance vector machines to large scaledatasets[J]. International Journal of Neural Systems(IJNS),2008,18(1):45-58
[62] Yuan J, Wang K.S, Yu T, Fang M.L. Integrating relevance vector machines and genetic algorithmsfor optimization of seed-separating process[J]. Engineering Applications of Artificial Intelligence,2007,20(7),970-979.
[63] Widodo A, Kim Eric Y, Son J.D, Yang B.S, Tan A.C.C, Gu D.S, et al. Fault diagnosis of low speedbearing based on relevance vector machine and support vector machine[J]. Expert Systems withApplications,2009,363,7252–7261.
[64]范伊红,李敏,张元.相关向量机在车型识别中的应用研究[J].计算机工程与设计,2008,29(6):1510-1512
[65]朱世增,党选举.基于相关向量机的非线性动态系统辨识[J].计算机仿真,2008,25(6):103-107
[66]刘遵雄,张德运,孙钦东,徐征.基于相关向量机的电力负荷中期预测[J].西安交通大学学报,2004,38(10):1005-1008
[67]王立坤,杨新锋.一种基于RVM回归的分类方法[J].电子科技,2011,24(5):14-17
[68]郦敏智,基于云模型的数据挖掘在入侵检测系统中的应用[D].广州:华南理工大学硕士学位论文,2004.
[69]阮耀平,易江波.计算机系统入侵检测模型与方法[J].计算机工程,1999,25(7):63-65
[70] Anderson J P. Computer Security Threat Monitoring and Surveillance.Pennsylvania[J]. USA: IEEEPress,1980,12-31
[71] Spafford E. Crisis and Aftermath.[J] Communications of the ACM,1989,32(6):678-687
[72] Smaha S, Haystack E. An Intrusion Detection System[A]. In: Proc of the4th Aerospace ComputerSecurity Applications Conference. Washington, USA: IEEEComputer Society Press[C],1988,37-44
[73] J. P. Anderson, Computer security threat monitoring and surveillance [J],Technical report,JamesP.Anderson Company,Fort Washington Pennsylvania,April1980.
[74] Dorothy.E, Denning. An Intrusion-Detection Model [J], IEEE Transactions on SoftwareEngineering,1987,13(2):23-34.
[75] Denning D. An Intrusion Detection Model[J]. IEEE Transactions on Software Engineering,1987,13(2):222-232
[76]唐正军,李建华.入侵检测技术[M].北京:清华大学出版社,2004,4-8
[77] Heberlein L T. A Network Security Monitor[J]. In: Proc of1990Symposium on Research inSecurity and Privacy, Oakland, USA: IEEE Press,1990,296-304
[78]唐正军.网络入侵检测系统的设计与实现[M].北京:电子工业出版社,2002,35-40
[79] Erbacher R F, Frincke D. Visualization in Detection of Intrusions and Misuse Inlarge ScaleNetworks[A]. In: Proc of the International Conference on Information Visualization. Los Alamitos, USA:IEEE Computer Society Press[C],2000,294-299
[80] Kruegel C, Mutz D, Robertson W, et a1. Bayesian Event Classification for Intrusion Detection[A].In:Proc of the19th Annual Computer Security Applications Conference(ACSAC). Los Alamitos, USA:IEEE Computer Society Press[C],2003,14-23
[81] Kruegel C, Toth T. Using Decision Trees to Improve Signature Based Intrusion Detection[J]. In:Proc of RAID2003. Berlin, Germany: Springer Verlag Press,2003,173-191
[82] Lee W. A Data Mining Fraine Work for Constricting Feature and Models for Intrusion DetectionSystem[D]. Columbia University,1999.
[83]蒋建春,马恒太,任党恩.网络安全入侵检测研究综述[J].软件学报,2000,11(11):1460-1466
[84]徐一帆.入侵检测中数据预处理算法的研究[D].沈阳:东北大学硕士学位论文,2007,20-38
[85]杨孔雨.计算智能及其在网络信息安全中的应用[J].微型机与应用,2003,7:4-6.
[86] Heady R, Luger G, Maccabe A. The Architecture of a Network Level Intrusion DetectionSystem[M]. Technical Report La-Sub-93-219, Department of Computer Science, University of NewMexico,1990
[87] Valdes A, Skinner K. Adaptive Model-based Monitoring for Cyber Attack Detection[J]. LectureNotes in Computer Science,2000,1907:80-93
[88]凌军,曹阳,尹建华.基于时态知识模型的网络入侵检测方法研究[J].计算机学报,2003,26(11):1591-1597
[89] Wenke L, Stolfo S. Data Mining Approaches for Intrusion Detection[J]. In: Proc of the7th UsenixSecurity Symposium. San Antonio, USA: IEEE Press,2003,74-81
[90] Song H Y, Lockwood J W. Efficient Packet Classification for Network Intrusion Detection UsingFPGA[A]. In: Proc of the13th Symp on Field-Programmable Gate Arrays. New York, USA: ACMPress[C],2005,238-245
[91] Rawat S, Pujari A K, Gulati V P. On the Use of Singular Value Decomposition for a Fast IntrusionDetection System[J]. Electronic Notes in Theoretical Computer Science,2006,142(3):215-228
[92] Wei L, Issa T. Unsupervised Anomaly Detection Using an Evolutionary Extension of K-meansAlgorithm[J]. International Journal of Information and Computer Security (IJICS),2008,2(2):107-139
[93] Eckmann S T, Vigna G, Kemmerer R A. An Attack Language for State-based Intrusion Detection[J].Journal of Computer Security,2002,10(2):71-104
[94]皮建勇,刘心松,吴艾.基于空间扩维特征的主机入侵检测模型[J].计算机辅助设计与图形学学报,2007,19(11):1482-1488
[95]谢逸,余顺争.基于Web用户浏览行为的统计异常检测[J].软件学报,2007,18(4):967-977
[96] Forrest S, Hofmeyr S, Somayaji A. A Sense of Self for Unix Processes [J]. In: Proc of the1996IEEE Symposium on Security and Privacy. Los Alamitos, USA: IEEE Computer Society Press,1996,120-128
[97] Wespi A, Dacier M, Debar H. Intrusion Detection Using Variable-length Audit Trail Pattern[J],Berlin, Germany: RAID Press,2000,110-129
[98]陶新民,陈万海,郭黎利.一种新的基于模糊聚类和免疫原理的入侵监测模型[J].电子学报,2006,34(7):1329-1332
[99]刘衍珩,田大新,余雪岗.基于分布式学习的大规模网络入侵检测算法[J].软件学报,2008,19(4):993-1003
[100] Bace, Rebacca. An Introduction to Intrusion Detection Assessment White Paper.http://www.icsalabs.com/html/communities/ids/whitepaper/Intrusion1.pdf,1999-1-4
[101] Sekar R, Guang Y, Verma S. A High-Performance Network Intrusion Detection System[J].In: Procof the6th conference on Computer and Communication Security. New York, USA: IEEE ComputerSociety Press,2002,8-17
[102] Christopher K, Fredrik V, Giovanni V. Stateful Intrusion Detection for High-speed Networks[J]. In:Proc of the2002IEEE Symposium on Security andPrivacy. Washington, USA: IEEE Computer SocietyPress,2002,285-293
[103]段海新,昊建平.一种分布式协同入侵检测系统的设计与实现[J].软件学报,2001,12(09):1375-1379
[104]陈碗,安常青,李学农.分布式入侵检测系统及其认知能力[J].软件学报,2001,12(2):225-232
[105]马恒太,蒋建春,陈伟锋.基于Agent的分布式入侵检测系统模型[J].软件学报,2000,11(10):1312-1319
[106]胡华平,张怡,陈海涛.面向大规模网络的入侵检测与预警系统研究[J].国防科技大学学报,2003,25(1):21-25
[107]徐氰,刘宝旭,许格生.基于数据挖掘技术的入侵检测系统设计与实现[J].计算机工程,2002,28(6):9-11
[108]史美林,钱俊,董永乐.入侵检测技术与发展趋势[J].信息安全与通信,2002,5(17):12-16
[109] Kemmerer, R.A, Vigna, Giovanni. Hi-DRA: Intrusion Detection for Internet Security[J].Proceedings of the IEEE,2005,93(10):1848-1857.
[110] Nong Ye, Xiangyang Li, Qiang Chen, Emran, S.M, Mingming Xu. Probabilistic techniques forintrusion detection based on computer audit data[J]. IEEE Systems, Man and Cybernetics Society,2001,31(4):266-274.
[111] Parikh, D, Tsuhan Chen. Data Fusion and Cost Minimization for Intrusion Detection[J].Information Forensics and Security, IEEE Transactions on,2008,3,(3):381-389.
[112] Sooyeon Shin, Taekyoung Kwon, Gil-Yong Jo, Youngman Park, Rhy, H. An Experimental Study ofHierarchical Intrusion Detection for Wireless Industrial Sensor Networks[J]. Industrial Informatics,IEEETransactions on,2010,6(4):744-757.
[113] Tavallaee M, Stakhanova N, Ghorban A.A. Toward Credible Evaluation of Anomaly-BasedIntrusion-Detection Methods[J]. Systems, Man, and Cybernetics, Part C: Applications and Reviews,IEEE Transactions on,2010,40(5):516-524.
[114] Tartakovsky A.G, Rozovskii B.L, Blazek R.B, Hongjoong Kim. A novel approach to detection ofintrusions in computer networks via adaptive sequential and batch-sequential change-point detectionmethods[J]. Signal Processing, IEEE Transactions on,2006,54(9):3372-3382.
[115] Zhenwei Yu, Tsai J.J.P, Weigert T. An Automatically Tuning Intrusion Detection System[J].Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on,2007,37(2):373-384.
[116] Mukherjee B, Heberlein L.T, Levitt K.N. Network intrusion detection[J]. Network, IEEE,1994,8(3):26-41.
[117]Ryutov T, Neuman C, Dongho Kim, Li Zhou. Integrated access control and intrusion detection forWeb servers[J]. Parallel and Distributed Systems, IEEE Transactions on,2003,14(9):841-850.
[118] Macia-Perez R, Mora-Gimeno R, Marcos-Jorquera D, Gil-Martinez-Abarca J.A, Ramos-Morillo H,Lorenzo-Fonseca I. Network Intrusion Detection System Embedded on a Smart Sensor[J]. IndustrialElectronics, IEEE Transactions on,2011,58(3):722-732.
[119] Colomi A, Dorigo M, Maniezzo V. Distributed optimization by antcolonies[A]. Proceedings of theFirst European Conference on Artificial Life. Elsevier Publishing,Paris, France[C],1991,134-142.
[120] Dorigo M, Gambardell LM. A Cooperative Learning Approach to the Traveling SalesmanProblem[J]. IEEE Trans on Evolutionary Computation,1997(9):33-35
[121]龚纯,王正林.精通Matlab最优化计算[M].北京:电子工业出版社,2009.
[122]黄美玲,白似雪.蚁群神经网络在旅行商问题中的应用[J].计算机辅助设计与图形学学报,2007,19(5):600-603
[123]段海滨.蚁群算法原理及应用[M].北京:科学出版社,2005
[124]李德毅,刘常昱.论正态云模型的普适性[J].中国工程科学,2004,6(8):28-34
[125]李士勇.工程模糊数学及应用[M].哈尔滨:哈尔滨工业大学出版社,2004,70-81
[126]柳炳祥,李海林.基于云理论与神经网络集成的模糊系统[J].计算机应用,2008,28(2):305-310
[127]刘理争,柴乔林.基于神经网络与证据理论的入侵检测系统[J].计算机工程与设计,2005,26(3):698-701
[128]张人上.基于专家系统和神经网络的网络入侵检测系统[J].计算机仿真,2012,29(9):162-165
[129]赵胜颖,高广春.基于蚁群算法的选择性神经网络集成方法[J].浙江大学学报,2009,43(9):1568-1573
[130]李京林.基于蚁群算法的改进神经网络在变压器故障辨识中的应用研究[D].南京:南京理工大学硕士学位论文,2007
[131] Marco Dorigo, Eric Bonabeau, Guy Theranlaz. Ant Algorithms and Stigmergy[J]. FutureGeneration Computer System,2000
[132] V. Katja, N. Ann. Colonies of Learning Automata[J].IEEE Transactions on Systems, Man, andCybernetics Part B,2002
[133] Nelloeristianini, JohnShawe Taylor著.李国正,王猛,曾华军译.支持向量机导论[M].北京:电子工业出版社,2002:78
[134]任红娥,霍满冬.基于PSO优化的SVM预测应用研究[J].计算机应用研究,2009,26(3):867-869
[135]邓乃扬,田英杰.数据挖掘中的新方法-支持向量机[M].北京:科学出版社,2004
[136]黄勇,郑春颖,宋忠虎.多类支持向量机算法综述[J].计算技术与自动化,2005,24(4):61-63.
[137]苟博,黄贤武.支持向量机多类分类方法[J].数据采集与处理,.2006,21(3):334-339.
[138] Hsu C W, Lin C J.A. Comparison of methods for multiclass support vector machines[J]. IEEETransaetions on Neural Networks.2002,Vol.13,No.2:415-425.
[139] RifkinR, ClautauA. Indefense of one-vs-all classifieation[J].Journal of Machine LearningReseareh.2004,5:101-141.
[140] Debnath R, Takahide N, Takahashi H. A decision based one-against-one method for mu1ti-class support vector machine[J]. Pattern Analysis and Application.2004,7(2):164-175.
[141] Krebel U. Pairwise classification and support vector machines.In:Scho1kopf B, Burges CJC,Smola A J, ed. Advances in Kerne l Methods-Support Vector Learning[M]. Cambridge, USA: MITPress,1999:255-268.
[142]Takahashi F, Abe S.Decision-tree-based multiclass support vector machines[A].In:Wang L,Rajapakse J C,Fukushima K, ed.Proeeedings of9thInternational Conference on Neural InformationProeessing[C],2002,3:1418-1422.
[143] Wang X D, Shi Z H, Wu C M et al.An improved algorithm for decision-tree-based SVM[A].6thWorld Congress on Intelligent Control and Automation, Dalian China[C],2006:4234-4238.
[144] Wang A N, Liu J F, Wang H et al.A novel fault diagnosis of analog circuit algorithm based onincomplete wavelet packet transform and improved balanced binary-tree SVMs[J].Bio-InspiredComputational Intelligence and Applications.2007,4688:482-493.
[145] Lorena A C, Carvalho A C P L F.Protein cellular loealization prediction with support vectormachines and decision trees[J].Computers in Biology and Medicine.2007,37:115-125.
[146] Hao P Y, Chiang J H, Tu Y K.Hierarchically SVM classifieation based on support vector clusteringmethod and its application to document categorization[J].Expert Systems with Applications.2007,33:627-635.
[147] Fei B, Liu J.Binary tree of SVM: a new fast multiclass training and classification algorithm[J].IEEE Transaction on neural Network.2006,17(30):696-704.
[148]Casasent D, Wang Y C.A hierarehical classifier using new spport vector maehines for automatictarget recognition[J].Neural networks.2005,18:541-548.
[149]陈增照,杨扬,何秀玲等.基于相似二叉树的支持向量机多类分类方法[A].6thworld Congress onIntelligent Control and Automation, Dalian China[C],2006:10041-10044.
[150]李雅琴,董才林,陈增照一种新的基于SVM的多层分类方法[J].计算机工程与科学.2007,29(4):108-110.
[2]史忠植.知识发现[M].北京:清华大学出版社,2002
[3]张乃尧,阎平凡.神经网络与模糊控制[M].北京:清华大学出版社,1999
[4]焦李成,慕彩红,王伶.通信中的智能信号处理[M].北京:电子工业出版社,2006
[5]白鹏,张喜斌,张斌等.支持向量机理论及工程应用实例[M].西安:西安电子科技大学出版社,2007
[6] Tipping M E, Faul A C. Fast Marginal Likelihood Maximization for Sparse Bayesian Models[A].KeyWest, FL: In CM Bishop, BJ Frey (Eds.), Proceedings of the Ninth International Workshop on ArtificialIntelligence and Statistics[C],2003,3-6
[7] Tipping M E. Sparse Bayesian Learning and the Relevance Vector Machine [J].Journal of machinelearning research.2001,1(3):211-244.
[8]董争.相关向量机学习的研究[J].技术与市场,2009,16(4):39-40
[9]杨树仁,沈宏远.基于相关向量机的机器学习算法研究与应用[J].计算技术与自动化,2010,29(1):43-47
[10]杨国鹏,周欣,余旭初.稀疏贝叶斯模型与相关向量机学习研究[J].计算机科学,2010,37(7):225-228
[11] Lee W, Xiang D. Information-Theoretic Measures for Anomaly Detection[A].In Proceedings of the2001IEEE Symposium on Security and Privacy.May[C],2001,130-143.
[12] Lane T. Machine learning techniques for the computer security domain of anomaly detection[D].Purdue University,2000.
[13] Matthew G, et al. Data mining methods for detection of new malicious executables[A]. Proceedingsof IEEE Symposium on Security and Privacy(IEEE S&P-2001)[C], Oakland,CA: May2001.14-17.
[14]郑成兴.网络入侵防范的理论与实践[M].北京:国防工业出版社,2006.
[15]龙冬阳.网络安全技术及应用[M].广州:华南理工大学出版社,2006.
[16]李剑.入侵检测技术[M].北京:高等教育出版社,2008.
[17]杨智君,田地,马骏骁.入侵检测技术研究综述[J].计算机工程与设计,2006,27(12):2119-2123
[18]史天运,贾利民.计算智能理论及其在RITS中的应用[J].交通运输系统工程与信息,2002,2(1):10-15.
[19]徐宗本,张讲社,郑亚林.计算智能中的仿生学:理论与算法[M].北京:科学出版社,2003.
[20]李澄非.计算智能方法研究及其在流程工业中应用[D].北京:北京化工大学博士学位论文,2007.
[21] David Shilane, Jarno Martikainen, Sandrine Dudoit, et al. A general framework for statisticalperformance comparison of evolutionary computation algorithms[J]. Information Sciences,2008,178(14-15):2870-2879.
[22]余有明,刘玉树.进化计算的理论与算法[J].计算机应用研究.2005,22(9):77-80.
[23]彭祖赠,孙韫玉.模糊(Fuzzy)数学及其应用[M].武汉:武汉大学出版社,2007.
[24] Amit Kumar Banerjee, K. Kiran, U.S.N. Murty, Ch. Venkateswarlu. Classification and identificationof mosquito species using artificial neural networks[J]. Computational Biology and Chemistry,2008,32(6):442-447.
[25]阎平凡,张长水,人工神经网络与模拟进化计算[M].北京:清华大学出版社,2000.
[26]丁永生.计算智能理论、技术与应用[M].北京:科学出版社,2004.
[27] Bart Baesens, Rudy Setiono, Christophe Meues, et al. Using Neural Network Rule Extraction andDecision Tables for Credit-Risk Evaluation[J]. Management Science,2003,49(3):312-329.
[28]王凌,智能优化算法及其应用[M].北京:清华大学出版社,2001.
[29]江沛.智能优化技术的研究及其在化工生产中的应用[D].北京:北京化工大学硕士学位论文,2008.
[30]罗熊,孙增圻.计算智能方法优化设计模糊控制系统:现状与展望[J].控制与决策,2007,22(9):961-966.
[31]盖斐,赵建玉,贾磊.基于智能计算的干线多路口交通信号控制[J].济南大学学报(自然科学版),2008,22(2):137-144.
[32]苏建元.计算智能主要算法的比较与融合[J].中国电子科学研究院学报,2007,2(1):52-56.
[33]马利.基于数据挖掘的聚类分析和传统聚类分析的对比研究[J].数理医药学杂志,2008,21(5):530-531
[34]黄利文.数据挖掘中的聚类分析方法[J].电脑知识与技术,2008,12(2):564-566
[35]魏晓云.决策树分类方法研究[J].计算机系统应用,2007,16(9):42-45
[36]魏红宁.基于SPRINT方法的并行决策树分类研究[J].计算机应用,2005,25(1):39-41
[37]段青.基于稀疏贝叶斯学习方法的回归与分类在电力系统中的预测研究[D].济南:山东大学博士学位论文,2010
[38]冀俊忠,刘椿年,阎静.一种快速的贝叶斯网结构学习算法[J].计算机研究与发展,2007,44(3):412-419
[39]范劲松,方廷建.基于粗集理论和SVM算法的模式分类算法[J].模式识别和人工智能,2000,13(4):419-423
[40] Andrew R.J. Clark, Richard M. Everson. Multi-objective learning of Relevance Vector Machineclassifiers with multi-resolution kernels[J].Pattern Recognition,2012,45(9):3535-3543
[41] Clodoaldo A.M. Lima, AndréL.V. Coelho, Sandro Chagas. Automatic EEG signal classification forepilepsy diagnosis with Relevance Vector Machines[J]. Expert Systems with Applications,2009,36(6):10054-10059.
[42] Xiaodong Wang, Meiying Ye, C.J. Duanmu. Classification of data from electronic nose usingrelevance vector machines[J]. Sensors and Actuators B: Chemical,2009,140(1):143-148
[43] MCCULLOCH W S, PITTS W H. A logical calculus of the ideas immanent in neuron activity[J].Bulletin Mathematical Biophysics,1943:115-133.
[44] WIDROW B. An adaptive adaline neuron using chemical memistors[M]. Standford ElectronicsLavoratroy Technical Report,1960.
[45] RUMELLHART D E, MCCLLELAND J L.Parallel distributed processing [M]. Cambridge, MA:MIT Press.1986.
[46] SAITO K, NAKANO R. Medical diagnostic expert system based on PDP mode [J].IEEEinternational conf.on Neural Networks,1988(1):225-262.
[47]王刚,黄丽华,张成洪,夏洁.数据挖掘分类算法研究综述[J].科技导报,2006,24(12):73-76
[48]葛黎辉.支持向量机及其金融应用[D].南京:南京农业大学大学硕士学位论文,2009.
[49]杨斌,匡立春,孙中春,等.一种用于测井油气层综合识别的支持向量机方法[J].测井技术,2005,29(12):511-514
[50]邓乃扬.支持向量机——理论、算法与拓展[M].北京:科学出版社,2009
[51] Osuna E.Freund,R.Cirusi,etc. Training algorithm for support vector machines[A].In:7th IEEEworkshop on Neural Networks for Signal Proeessing[C],1997,276-285.
[52] Joaehims,T.Making Large-Scale SVM Leaming Practical[J].Cambridge,MA:MIT press,1995,169-154.
[53] J.platt.Fast training of support vector machines using sequential minimal optimization[J].Cambridge,MA:MIT Press,1999,185-208.
[54] Lin C J.Asymptotic convergence of an SMO algorithm without any assumptions[A].IEEETrans.Neural News[C].2002.
[55] S.S.Keerthi, etc. Improvements to platt’s SMO Algorithm for SVM Classifier Design[J]. MIT PressJournals,2001,13(3):637-649.
[56] S.S.Keerthi, etc. Convergence of a Generalized SMO Algorithm for SVM Classifier Design[J].Machine Learning,2002,46(1-3):351-360.
[57] J-X.Dong,A.Krzyzak,and C.Y.Suen. Fast SVM Training Algorithm with Decomposition on VeryLarge Data sets[J]. IEEE Trans.Pattern Analysis and Machine Intelligence,2005,27(4),603-618.
[58] J.Cervantes etc.Support Vector Machine Classification Based on Fuzzy Clustering for LargeDatasets[J].MICAI2006:Advances in Artificial Intelligence,Srpinger-Verlgag,2006,4293,572-582.
[59] XiaoOu Li, J Cervantes, and Wen Yu.Two-stage SVM classification for large datasets via randomlyreducing and recovering training data[A]. IEEE International Conference, Systems, Man andCybernetics[C],2007,3633-3638.
[60] Zheng Rong Yang. A Fast Algorithm for Relevance Vector Machine[A].7th International Conferenceon Intelligent Data Engineering and Automated Learning, IDEAL2006, IEEE press[C],2006,33-39
[61] Catarina Sliva, Bernardete Ribeiro. Towards expanding relevance vector machines to large scaledatasets[J]. International Journal of Neural Systems(IJNS),2008,18(1):45-58
[62] Yuan J, Wang K.S, Yu T, Fang M.L. Integrating relevance vector machines and genetic algorithmsfor optimization of seed-separating process[J]. Engineering Applications of Artificial Intelligence,2007,20(7),970-979.
[63] Widodo A, Kim Eric Y, Son J.D, Yang B.S, Tan A.C.C, Gu D.S, et al. Fault diagnosis of low speedbearing based on relevance vector machine and support vector machine[J]. Expert Systems withApplications,2009,363,7252–7261.
[64]范伊红,李敏,张元.相关向量机在车型识别中的应用研究[J].计算机工程与设计,2008,29(6):1510-1512
[65]朱世增,党选举.基于相关向量机的非线性动态系统辨识[J].计算机仿真,2008,25(6):103-107
[66]刘遵雄,张德运,孙钦东,徐征.基于相关向量机的电力负荷中期预测[J].西安交通大学学报,2004,38(10):1005-1008
[67]王立坤,杨新锋.一种基于RVM回归的分类方法[J].电子科技,2011,24(5):14-17
[68]郦敏智,基于云模型的数据挖掘在入侵检测系统中的应用[D].广州:华南理工大学硕士学位论文,2004.
[69]阮耀平,易江波.计算机系统入侵检测模型与方法[J].计算机工程,1999,25(7):63-65
[70] Anderson J P. Computer Security Threat Monitoring and Surveillance.Pennsylvania[J]. USA: IEEEPress,1980,12-31
[71] Spafford E. Crisis and Aftermath.[J] Communications of the ACM,1989,32(6):678-687
[72] Smaha S, Haystack E. An Intrusion Detection System[A]. In: Proc of the4th Aerospace ComputerSecurity Applications Conference. Washington, USA: IEEEComputer Society Press[C],1988,37-44
[73] J. P. Anderson, Computer security threat monitoring and surveillance [J],Technical report,JamesP.Anderson Company,Fort Washington Pennsylvania,April1980.
[74] Dorothy.E, Denning. An Intrusion-Detection Model [J], IEEE Transactions on SoftwareEngineering,1987,13(2):23-34.
[75] Denning D. An Intrusion Detection Model[J]. IEEE Transactions on Software Engineering,1987,13(2):222-232
[76]唐正军,李建华.入侵检测技术[M].北京:清华大学出版社,2004,4-8
[77] Heberlein L T. A Network Security Monitor[J]. In: Proc of1990Symposium on Research inSecurity and Privacy, Oakland, USA: IEEE Press,1990,296-304
[78]唐正军.网络入侵检测系统的设计与实现[M].北京:电子工业出版社,2002,35-40
[79] Erbacher R F, Frincke D. Visualization in Detection of Intrusions and Misuse Inlarge ScaleNetworks[A]. In: Proc of the International Conference on Information Visualization. Los Alamitos, USA:IEEE Computer Society Press[C],2000,294-299
[80] Kruegel C, Mutz D, Robertson W, et a1. Bayesian Event Classification for Intrusion Detection[A].In:Proc of the19th Annual Computer Security Applications Conference(ACSAC). Los Alamitos, USA:IEEE Computer Society Press[C],2003,14-23
[81] Kruegel C, Toth T. Using Decision Trees to Improve Signature Based Intrusion Detection[J]. In:Proc of RAID2003. Berlin, Germany: Springer Verlag Press,2003,173-191
[82] Lee W. A Data Mining Fraine Work for Constricting Feature and Models for Intrusion DetectionSystem[D]. Columbia University,1999.
[83]蒋建春,马恒太,任党恩.网络安全入侵检测研究综述[J].软件学报,2000,11(11):1460-1466
[84]徐一帆.入侵检测中数据预处理算法的研究[D].沈阳:东北大学硕士学位论文,2007,20-38
[85]杨孔雨.计算智能及其在网络信息安全中的应用[J].微型机与应用,2003,7:4-6.
[86] Heady R, Luger G, Maccabe A. The Architecture of a Network Level Intrusion DetectionSystem[M]. Technical Report La-Sub-93-219, Department of Computer Science, University of NewMexico,1990
[87] Valdes A, Skinner K. Adaptive Model-based Monitoring for Cyber Attack Detection[J]. LectureNotes in Computer Science,2000,1907:80-93
[88]凌军,曹阳,尹建华.基于时态知识模型的网络入侵检测方法研究[J].计算机学报,2003,26(11):1591-1597
[89] Wenke L, Stolfo S. Data Mining Approaches for Intrusion Detection[J]. In: Proc of the7th UsenixSecurity Symposium. San Antonio, USA: IEEE Press,2003,74-81
[90] Song H Y, Lockwood J W. Efficient Packet Classification for Network Intrusion Detection UsingFPGA[A]. In: Proc of the13th Symp on Field-Programmable Gate Arrays. New York, USA: ACMPress[C],2005,238-245
[91] Rawat S, Pujari A K, Gulati V P. On the Use of Singular Value Decomposition for a Fast IntrusionDetection System[J]. Electronic Notes in Theoretical Computer Science,2006,142(3):215-228
[92] Wei L, Issa T. Unsupervised Anomaly Detection Using an Evolutionary Extension of K-meansAlgorithm[J]. International Journal of Information and Computer Security (IJICS),2008,2(2):107-139
[93] Eckmann S T, Vigna G, Kemmerer R A. An Attack Language for State-based Intrusion Detection[J].Journal of Computer Security,2002,10(2):71-104
[94]皮建勇,刘心松,吴艾.基于空间扩维特征的主机入侵检测模型[J].计算机辅助设计与图形学学报,2007,19(11):1482-1488
[95]谢逸,余顺争.基于Web用户浏览行为的统计异常检测[J].软件学报,2007,18(4):967-977
[96] Forrest S, Hofmeyr S, Somayaji A. A Sense of Self for Unix Processes [J]. In: Proc of the1996IEEE Symposium on Security and Privacy. Los Alamitos, USA: IEEE Computer Society Press,1996,120-128
[97] Wespi A, Dacier M, Debar H. Intrusion Detection Using Variable-length Audit Trail Pattern[J],Berlin, Germany: RAID Press,2000,110-129
[98]陶新民,陈万海,郭黎利.一种新的基于模糊聚类和免疫原理的入侵监测模型[J].电子学报,2006,34(7):1329-1332
[99]刘衍珩,田大新,余雪岗.基于分布式学习的大规模网络入侵检测算法[J].软件学报,2008,19(4):993-1003
[100] Bace, Rebacca. An Introduction to Intrusion Detection Assessment White Paper.http://www.icsalabs.com/html/communities/ids/whitepaper/Intrusion1.pdf,1999-1-4
[101] Sekar R, Guang Y, Verma S. A High-Performance Network Intrusion Detection System[J].In: Procof the6th conference on Computer and Communication Security. New York, USA: IEEE ComputerSociety Press,2002,8-17
[102] Christopher K, Fredrik V, Giovanni V. Stateful Intrusion Detection for High-speed Networks[J]. In:Proc of the2002IEEE Symposium on Security andPrivacy. Washington, USA: IEEE Computer SocietyPress,2002,285-293
[103]段海新,昊建平.一种分布式协同入侵检测系统的设计与实现[J].软件学报,2001,12(09):1375-1379
[104]陈碗,安常青,李学农.分布式入侵检测系统及其认知能力[J].软件学报,2001,12(2):225-232
[105]马恒太,蒋建春,陈伟锋.基于Agent的分布式入侵检测系统模型[J].软件学报,2000,11(10):1312-1319
[106]胡华平,张怡,陈海涛.面向大规模网络的入侵检测与预警系统研究[J].国防科技大学学报,2003,25(1):21-25
[107]徐氰,刘宝旭,许格生.基于数据挖掘技术的入侵检测系统设计与实现[J].计算机工程,2002,28(6):9-11
[108]史美林,钱俊,董永乐.入侵检测技术与发展趋势[J].信息安全与通信,2002,5(17):12-16
[109] Kemmerer, R.A, Vigna, Giovanni. Hi-DRA: Intrusion Detection for Internet Security[J].Proceedings of the IEEE,2005,93(10):1848-1857.
[110] Nong Ye, Xiangyang Li, Qiang Chen, Emran, S.M, Mingming Xu. Probabilistic techniques forintrusion detection based on computer audit data[J]. IEEE Systems, Man and Cybernetics Society,2001,31(4):266-274.
[111] Parikh, D, Tsuhan Chen. Data Fusion and Cost Minimization for Intrusion Detection[J].Information Forensics and Security, IEEE Transactions on,2008,3,(3):381-389.
[112] Sooyeon Shin, Taekyoung Kwon, Gil-Yong Jo, Youngman Park, Rhy, H. An Experimental Study ofHierarchical Intrusion Detection for Wireless Industrial Sensor Networks[J]. Industrial Informatics,IEEETransactions on,2010,6(4):744-757.
[113] Tavallaee M, Stakhanova N, Ghorban A.A. Toward Credible Evaluation of Anomaly-BasedIntrusion-Detection Methods[J]. Systems, Man, and Cybernetics, Part C: Applications and Reviews,IEEE Transactions on,2010,40(5):516-524.
[114] Tartakovsky A.G, Rozovskii B.L, Blazek R.B, Hongjoong Kim. A novel approach to detection ofintrusions in computer networks via adaptive sequential and batch-sequential change-point detectionmethods[J]. Signal Processing, IEEE Transactions on,2006,54(9):3372-3382.
[115] Zhenwei Yu, Tsai J.J.P, Weigert T. An Automatically Tuning Intrusion Detection System[J].Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on,2007,37(2):373-384.
[116] Mukherjee B, Heberlein L.T, Levitt K.N. Network intrusion detection[J]. Network, IEEE,1994,8(3):26-41.
[117]Ryutov T, Neuman C, Dongho Kim, Li Zhou. Integrated access control and intrusion detection forWeb servers[J]. Parallel and Distributed Systems, IEEE Transactions on,2003,14(9):841-850.
[118] Macia-Perez R, Mora-Gimeno R, Marcos-Jorquera D, Gil-Martinez-Abarca J.A, Ramos-Morillo H,Lorenzo-Fonseca I. Network Intrusion Detection System Embedded on a Smart Sensor[J]. IndustrialElectronics, IEEE Transactions on,2011,58(3):722-732.
[119] Colomi A, Dorigo M, Maniezzo V. Distributed optimization by antcolonies[A]. Proceedings of theFirst European Conference on Artificial Life. Elsevier Publishing,Paris, France[C],1991,134-142.
[120] Dorigo M, Gambardell LM. A Cooperative Learning Approach to the Traveling SalesmanProblem[J]. IEEE Trans on Evolutionary Computation,1997(9):33-35
[121]龚纯,王正林.精通Matlab最优化计算[M].北京:电子工业出版社,2009.
[122]黄美玲,白似雪.蚁群神经网络在旅行商问题中的应用[J].计算机辅助设计与图形学学报,2007,19(5):600-603
[123]段海滨.蚁群算法原理及应用[M].北京:科学出版社,2005
[124]李德毅,刘常昱.论正态云模型的普适性[J].中国工程科学,2004,6(8):28-34
[125]李士勇.工程模糊数学及应用[M].哈尔滨:哈尔滨工业大学出版社,2004,70-81
[126]柳炳祥,李海林.基于云理论与神经网络集成的模糊系统[J].计算机应用,2008,28(2):305-310
[127]刘理争,柴乔林.基于神经网络与证据理论的入侵检测系统[J].计算机工程与设计,2005,26(3):698-701
[128]张人上.基于专家系统和神经网络的网络入侵检测系统[J].计算机仿真,2012,29(9):162-165
[129]赵胜颖,高广春.基于蚁群算法的选择性神经网络集成方法[J].浙江大学学报,2009,43(9):1568-1573
[130]李京林.基于蚁群算法的改进神经网络在变压器故障辨识中的应用研究[D].南京:南京理工大学硕士学位论文,2007
[131] Marco Dorigo, Eric Bonabeau, Guy Theranlaz. Ant Algorithms and Stigmergy[J]. FutureGeneration Computer System,2000
[132] V. Katja, N. Ann. Colonies of Learning Automata[J].IEEE Transactions on Systems, Man, andCybernetics Part B,2002
[133] Nelloeristianini, JohnShawe Taylor著.李国正,王猛,曾华军译.支持向量机导论[M].北京:电子工业出版社,2002:78
[134]任红娥,霍满冬.基于PSO优化的SVM预测应用研究[J].计算机应用研究,2009,26(3):867-869
[135]邓乃扬,田英杰.数据挖掘中的新方法-支持向量机[M].北京:科学出版社,2004
[136]黄勇,郑春颖,宋忠虎.多类支持向量机算法综述[J].计算技术与自动化,2005,24(4):61-63.
[137]苟博,黄贤武.支持向量机多类分类方法[J].数据采集与处理,.2006,21(3):334-339.
[138] Hsu C W, Lin C J.A. Comparison of methods for multiclass support vector machines[J]. IEEETransaetions on Neural Networks.2002,Vol.13,No.2:415-425.
[139] RifkinR, ClautauA. Indefense of one-vs-all classifieation[J].Journal of Machine LearningReseareh.2004,5:101-141.
[140] Debnath R, Takahide N, Takahashi H. A decision based one-against-one method for mu1ti-class support vector machine[J]. Pattern Analysis and Application.2004,7(2):164-175.
[141] Krebel U. Pairwise classification and support vector machines.In:Scho1kopf B, Burges CJC,Smola A J, ed. Advances in Kerne l Methods-Support Vector Learning[M]. Cambridge, USA: MITPress,1999:255-268.
[142]Takahashi F, Abe S.Decision-tree-based multiclass support vector machines[A].In:Wang L,Rajapakse J C,Fukushima K, ed.Proeeedings of9thInternational Conference on Neural InformationProeessing[C],2002,3:1418-1422.
[143] Wang X D, Shi Z H, Wu C M et al.An improved algorithm for decision-tree-based SVM[A].6thWorld Congress on Intelligent Control and Automation, Dalian China[C],2006:4234-4238.
[144] Wang A N, Liu J F, Wang H et al.A novel fault diagnosis of analog circuit algorithm based onincomplete wavelet packet transform and improved balanced binary-tree SVMs[J].Bio-InspiredComputational Intelligence and Applications.2007,4688:482-493.
[145] Lorena A C, Carvalho A C P L F.Protein cellular loealization prediction with support vectormachines and decision trees[J].Computers in Biology and Medicine.2007,37:115-125.
[146] Hao P Y, Chiang J H, Tu Y K.Hierarchically SVM classifieation based on support vector clusteringmethod and its application to document categorization[J].Expert Systems with Applications.2007,33:627-635.
[147] Fei B, Liu J.Binary tree of SVM: a new fast multiclass training and classification algorithm[J].IEEE Transaction on neural Network.2006,17(30):696-704.
[148]Casasent D, Wang Y C.A hierarehical classifier using new spport vector maehines for automatictarget recognition[J].Neural networks.2005,18:541-548.
[149]陈增照,杨扬,何秀玲等.基于相似二叉树的支持向量机多类分类方法[A].6thworld Congress onIntelligent Control and Automation, Dalian China[C],2006:10041-10044.
[150]李雅琴,董才林,陈增照一种新的基于SVM的多层分类方法[J].计算机工程与科学.2007,29(4):108-110.