网络安全定量风险评估及预测技术研究
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摘要
目前,互联网成为国家的重要信息基础设施,互联网安全问题也成为事关国家安全的战略性问题。因此,开展网络安全风险评估理论及其关键技术研究具有重要的理论意义和实用价值。
由于互联网具有复杂非线性系统特性,本文将非线性系统分析和预测技术引入到网络安全定量风险评估中,探讨如何针对风险评估中相关要素的复杂关系以及不确定性特点,建立科学的定量评估体系与评估方法。本文主要从三个方面开展研究工作:将非线性混沌和分形理论引入到网络威胁频率的复杂性分析中,以揭示蕴涵在网络威胁时序中的本质特征;研究了针对网络威胁频率的非线性混沌预测方法;设计实现了动态定量网络安全风险评估原型系统。
本文的主要成果和创新点如下:
(1)提出一种动态定量网络安全评估与预测(DN-SAP)引擎设计方案,DN-SAP引擎可以作为一种安全功能部件配置到网络基础设施中,以提高网络安全定量评估和预警能力。设计实现了一套网络威胁数据采集系统,并在某局域网和某公网分别采集了半年和近一个月的实际网络威胁数据,根据数据来源和时间特性为网络威胁频率研究建立了三个真实客观的、可供参考的数据集。
(2)提出一种基于R/S分析的网络威胁时序统计自相似性分析方法,通过计算并检验上述三个威胁数据集中选出的典型威胁时序样本的Hurst指数,验证了连续和间断的网络威胁时序具有统计自相似性,因此可预测性较好,而稀疏的威胁时序不具有统计自相似性,可预测性较差。
(3)提出一个融合多种非线性检验方法的网络威胁时序混沌性判别方法——混沌模型筛(CMS),综合集成了相空间重构、功率谱分析、最大Lyapunov指数、关联维数及其相位随机化等多种混沌性判别技术,可有效判定网络威胁时序的特性。同时研究了网络威胁频率的预测模型选择问题,给出了对随机模型、确定模型与混沌模型的选择准则。基于CMS对实测网络威胁频率时序样本进行了混沌性判别,结果表明网络威胁时序具有混沌性。
(4)提出一种针对基于最大Lyapunov指数预测法的改进算法——基于最佳邻近点发散指数预测法,通过对网络威胁时序样本的对比预测实验验证了改进算法的准确性较高。实际网络威胁频率样本预测结果表明,基于混沌预测方法的预测准确度要优于传统的统计预测方法。
(5)在上述理论与算法研究基础上,设计了一个多层次、协同式动态网络安全定量风险评估(MC-NSRA)体系结构,该系统构造方法符合互联网拓扑结构和流量模型的分形自相似性特点。为消除系统中由于脆弱性扫描而引发的“伪攻击”问题,提出了扫描权限概念,并基于属性证书设计了扫描权限证书及其管理机制,为脆弱性扫描管理提供了一条途径。
本文研究工作可为动态网络安全风险评估提供及时、定量的网络威胁预测数据,为决策者提供有参考价值的前瞻性数据,以辅助决策者制定有效的预防策略,避免信息损毁带来的经济损失和防护过度造成的高成本投入。
Nowadays, the Internet has become an important information infrastructure for our society. Security problems on the Internet have also become strategic challenges for national security. Therefore, studies on the theories and key technologies of risk assessment for network security have great theoretical significance and practical values.
Due to the complex nonlinear system properties of the Internet, this dissertation makes attempts to study quantitative risk assessment for network security by making use of nonlinear system analysis and prediction techniques. It aims at exploring the complexity and uncertainty relationships among the elements in risk assessment and to establish a framework and method for quantitative risk assessment. Three aspects of research works are mainly conducted in this dissertation. One aspect is to introduce nonlinear system theories on chaos and fractal for complexity analysis of network threat frequencies and to reveal the essential characters in the network threat time series. The second aspect is to study nonlinear chaotic prediction methods for network threat frequencies. The third aspect is to design a prototype system for dynamic quantitative risk assessment of network security.
The main results and contributions in this dissertation are as follows:
(1) A design project of Dynamic quaNtitative network Security risk Assessment and Prediction (DN-SAP) engine was proposed, which can be integrated into the network infrastructure as a security component. Therefore, the ability of quantitative risk assessment of network security and early warning can be improved. In DN-SAP, a data collection system for network threat was designed and implemented. The data collection system was used in a local area network and a public network for half a year and for one month, respectively, and three real threat data sets were constructed for the research on network threat frequencies.
(2) A fractal self-similarity analysis method for network threat time series based on the R/S (Rescaled range) analysis was proposed. Using this method, the Hurst exponent of the representative samples from the three data sets of network threat were computed and tested. It is verified that there exist statistic self-similarities in continuous and non-sparse discrete time series of network threat so that it will be feasible to predict. On the other hand, there is no statistic self-similarity in sparse discrete threat time series and it will be very difficult to predict.
(3) A hybrid determination method of chaos for time series of network threat named Chaotic Model Sieve (CMS) was proposed based on metasynthesis many nonlinearity test methods such as reconstruction of phase space, power spectrum, maximum Lyapunov exponent, correlation dimension and phase randomization. It was shown that the proposed method can determine the properties of network threat time series effectively. The model selection problem for prediction of threats frequencies was also studied and a criterion for selecting random, deterministic and chaotic models was provided. The experimental results of testing the samples from network threat data sets by CMS show that the time series of network threat are chaotic.
(4) A prediction method based on the divergence exponent of the best neighbor was proposed, which was aimed at improving the method based on the largest Lyapunov exponent. It is validated that the accuracy of the proposed method was higher than the primary method by contrast experiment of predicting the time series samples of the network threat frequencies. The experimental results of predicting the samples also show that the accuracy of chaotic prediction method exceeds the traditional statistical prediction method.
(5) Based on the above works, architecture of Multilayer Cooperative dynamic Network Security quantitative Risk Assessment (MC-NSRA) was designed. The construction of the system is conformed to the self-similarity in the topology and traffic models of the Internet. In order to eliminate the "pseudo attack" caused by vulnerability scan in risk assessment system, a new concept of scan authority was proposed. The scan authority certificate and its management mechanism were designed based on the Attribute Certificate. It gives a new way to manage the vulnerability scan.
The research work in this dissertation can be used to provide timely, quantitative prediction data of network threat for dynamic network security risk assessment, which provides valuable forecasting data for decision maker, and aid to establish effective defense strategy. It can be expected that the applications of the methods proposed in this dissertation will contribute to avoid economic losses from information damaged and high investments for unnecessary defense actions.
由于互联网具有复杂非线性系统特性,本文将非线性系统分析和预测技术引入到网络安全定量风险评估中,探讨如何针对风险评估中相关要素的复杂关系以及不确定性特点,建立科学的定量评估体系与评估方法。本文主要从三个方面开展研究工作:将非线性混沌和分形理论引入到网络威胁频率的复杂性分析中,以揭示蕴涵在网络威胁时序中的本质特征;研究了针对网络威胁频率的非线性混沌预测方法;设计实现了动态定量网络安全风险评估原型系统。
本文的主要成果和创新点如下:
(1)提出一种动态定量网络安全评估与预测(DN-SAP)引擎设计方案,DN-SAP引擎可以作为一种安全功能部件配置到网络基础设施中,以提高网络安全定量评估和预警能力。设计实现了一套网络威胁数据采集系统,并在某局域网和某公网分别采集了半年和近一个月的实际网络威胁数据,根据数据来源和时间特性为网络威胁频率研究建立了三个真实客观的、可供参考的数据集。
(2)提出一种基于R/S分析的网络威胁时序统计自相似性分析方法,通过计算并检验上述三个威胁数据集中选出的典型威胁时序样本的Hurst指数,验证了连续和间断的网络威胁时序具有统计自相似性,因此可预测性较好,而稀疏的威胁时序不具有统计自相似性,可预测性较差。
(3)提出一个融合多种非线性检验方法的网络威胁时序混沌性判别方法——混沌模型筛(CMS),综合集成了相空间重构、功率谱分析、最大Lyapunov指数、关联维数及其相位随机化等多种混沌性判别技术,可有效判定网络威胁时序的特性。同时研究了网络威胁频率的预测模型选择问题,给出了对随机模型、确定模型与混沌模型的选择准则。基于CMS对实测网络威胁频率时序样本进行了混沌性判别,结果表明网络威胁时序具有混沌性。
(4)提出一种针对基于最大Lyapunov指数预测法的改进算法——基于最佳邻近点发散指数预测法,通过对网络威胁时序样本的对比预测实验验证了改进算法的准确性较高。实际网络威胁频率样本预测结果表明,基于混沌预测方法的预测准确度要优于传统的统计预测方法。
(5)在上述理论与算法研究基础上,设计了一个多层次、协同式动态网络安全定量风险评估(MC-NSRA)体系结构,该系统构造方法符合互联网拓扑结构和流量模型的分形自相似性特点。为消除系统中由于脆弱性扫描而引发的“伪攻击”问题,提出了扫描权限概念,并基于属性证书设计了扫描权限证书及其管理机制,为脆弱性扫描管理提供了一条途径。
本文研究工作可为动态网络安全风险评估提供及时、定量的网络威胁预测数据,为决策者提供有参考价值的前瞻性数据,以辅助决策者制定有效的预防策略,避免信息损毁带来的经济损失和防护过度造成的高成本投入。
Nowadays, the Internet has become an important information infrastructure for our society. Security problems on the Internet have also become strategic challenges for national security. Therefore, studies on the theories and key technologies of risk assessment for network security have great theoretical significance and practical values.
Due to the complex nonlinear system properties of the Internet, this dissertation makes attempts to study quantitative risk assessment for network security by making use of nonlinear system analysis and prediction techniques. It aims at exploring the complexity and uncertainty relationships among the elements in risk assessment and to establish a framework and method for quantitative risk assessment. Three aspects of research works are mainly conducted in this dissertation. One aspect is to introduce nonlinear system theories on chaos and fractal for complexity analysis of network threat frequencies and to reveal the essential characters in the network threat time series. The second aspect is to study nonlinear chaotic prediction methods for network threat frequencies. The third aspect is to design a prototype system for dynamic quantitative risk assessment of network security.
The main results and contributions in this dissertation are as follows:
(1) A design project of Dynamic quaNtitative network Security risk Assessment and Prediction (DN-SAP) engine was proposed, which can be integrated into the network infrastructure as a security component. Therefore, the ability of quantitative risk assessment of network security and early warning can be improved. In DN-SAP, a data collection system for network threat was designed and implemented. The data collection system was used in a local area network and a public network for half a year and for one month, respectively, and three real threat data sets were constructed for the research on network threat frequencies.
(2) A fractal self-similarity analysis method for network threat time series based on the R/S (Rescaled range) analysis was proposed. Using this method, the Hurst exponent of the representative samples from the three data sets of network threat were computed and tested. It is verified that there exist statistic self-similarities in continuous and non-sparse discrete time series of network threat so that it will be feasible to predict. On the other hand, there is no statistic self-similarity in sparse discrete threat time series and it will be very difficult to predict.
(3) A hybrid determination method of chaos for time series of network threat named Chaotic Model Sieve (CMS) was proposed based on metasynthesis many nonlinearity test methods such as reconstruction of phase space, power spectrum, maximum Lyapunov exponent, correlation dimension and phase randomization. It was shown that the proposed method can determine the properties of network threat time series effectively. The model selection problem for prediction of threats frequencies was also studied and a criterion for selecting random, deterministic and chaotic models was provided. The experimental results of testing the samples from network threat data sets by CMS show that the time series of network threat are chaotic.
(4) A prediction method based on the divergence exponent of the best neighbor was proposed, which was aimed at improving the method based on the largest Lyapunov exponent. It is validated that the accuracy of the proposed method was higher than the primary method by contrast experiment of predicting the time series samples of the network threat frequencies. The experimental results of predicting the samples also show that the accuracy of chaotic prediction method exceeds the traditional statistical prediction method.
(5) Based on the above works, architecture of Multilayer Cooperative dynamic Network Security quantitative Risk Assessment (MC-NSRA) was designed. The construction of the system is conformed to the self-similarity in the topology and traffic models of the Internet. In order to eliminate the "pseudo attack" caused by vulnerability scan in risk assessment system, a new concept of scan authority was proposed. The scan authority certificate and its management mechanism were designed based on the Attribute Certificate. It gives a new way to manage the vulnerability scan.
The research work in this dissertation can be used to provide timely, quantitative prediction data of network threat for dynamic network security risk assessment, which provides valuable forecasting data for decision maker, and aid to establish effective defense strategy. It can be expected that the applications of the methods proposed in this dissertation will contribute to avoid economic losses from information damaged and high investments for unnecessary defense actions.
引文
[1]The International Organization for Standardization,Common Criteria for Information Technology Security Evaluation,ISO/IEC 15408:1999(E)
[2]Thomas R.Peltier.Information security risk analysis.Auerbach,2001
[3]翁文波预测科学网.http://www.wengs.com.cn
[4]翁文波.预测学.吕牛顿,张清编.北京:石油工业出版社,1996.11
[5]C.J.Alberts,A.J.Dorofee.OCTAVE~(SM) Method Implementation Guide.v2.0.Pittsburgh,PA:Software Engineering Institute,Carnegie Mellon University,2001
[6]The International Organization for Standardization,Code of Practice for Information Security Management,ISO/IEC 17799:2000,2000.12
[7]BSI/DISC Committee BDD/2,BS7799 Code of Practice for Information Security Management,1999
[8]The International Organization for Standardization,ISO/IEC TR 13335,Information technology-Guidelines for the Management of IT Security(GMITS),1996-2001
[9]The International Organization for Standardization,System Security Engineering Capability Maturity Model(SSE-CMM),ISO/IEC 21827:2002
[10]中国信息安全风险评估论坛.http://www.cisraf.infosec.org.cn
[11]关义章,戴宗坤 主编,罗万伯,周安民,谭兴烈等编著.信息系统安全工程学.北京:电子工业出版社,2002.12
[12]M.S.Lund,I.Hogganvik,F.Seehusen,K.Stolen.The CORAS framework,the CORAS UML profile for security assessment,and the CORAS library of reusable elements.IST-2000-25031.2003.9
[13]J.Aagedal,F.Braber,T.Dimitrakos,B.Gran,D.Raptis,K.Stolen.Model-based Risk Assessment to Improve Enterprise Security.IEEE.Published in the Proceedings of the Fifth International Enterprise Distributed Object Computing Conference(EDOC 2002),2002.9.51-62
[14]T.Dimitrakos,J.Bicarregui,K.Stolen.CORAS-a framework for risk analysis of security critical systems.ERCIM news,2002.number 49,25-26
[15]W.H.Sanders.Stochastic methods for dependability,performability,and security evaluation.Lecture Notes In Computer Science.2004.3099:97
[16]D.A.Hillson,D.T.Hulett.Assessing Risk Probability:Altemative Approaches,In Proc.2004 PMI Global Congress.2004
[17]Y.Chen,C.Jensen.A General Risk Assessment of Security in Pervasive Computing.Technical Report TCD-CS-2003-45,Department of Computer Science,Trinity College Dublin.2003.11
[18]R.C.Wilcox,B.M.Ayyub.Uncertainty Modeling of Data and Uncertainty Propagation for Risk Studies.Proceedings of the Fourth International Symposium on Uncertainty Modeling and Analysis(ISUMA'03),IEEE.2003
[19]Fang Liu,Yong Chen,Kui Dai,Zhiying Wang.Research on Risk Probability Estimating using Fuzzy Clustering for Dynamic Security Assessment.The Tenth International Conference on Rough Sets,Fuzzy Sets,Data Mining,and Granular Computing,Regina,Saskatchewan,Canada,Ivo Duentsch(Eds.):LNCS,Springer-Verlag,2005.9
[20]刘芳.信息系统安全评估理论及其关键技术研究.博士学位论文.国防科学技术大学.2005.4
[21]C&A Systems Security Ltd.COBRA:Introduction to Risk Analysis,http://www.casystems.zetnet.co.uk/risk.htm.2001
[22]List of Risk Analysis,Assessment and Management Tools.Audit Tools.Vol.1,1998.9
[23]Applied Computer Security Associates,The MITRE Corp..Workshop on Information Security System Rating and Ranking.2001.5
[24]Symantec.http://www.Symantec.com
[25]安络公司.http://www.CNNS.net
[26]W.Ozier.Risk assessment and management.In:T.R.Peltier.Information security risk analysis,Auerbach,2001.p223
[27]S.A.Butler.Security Attribute Evaluation Method.CMU-CS-03-132,Submitted in partial fulfillment of the requirements for the Degree of Doctor of Philosophy,School of Computer Science Carnegie Mellon University,Pittsburgh,PA 15213.2003.5
[28]B.D'Ambrosio,Masami Takikawa,Julie Fitzgerald,Daniel Upper,Suzanne Mahoney.Security Situation Assessment and Response Evaluation(SSARE).Proceedings of the DARPA Information Survivability Conference and Exposition (DISCEXII'01),IEEE.2001
[29]M.Ishiguro,Hironobu Suzuki,Ichiro Murase,Hiroyuki Ohno.Internet Threat Detection System Using Bayesian Estimation.FIRST'2004
[30]P.A.Porras.STAT:A State Transition Analysis Tool for Intrusion Detection.Master's Thesis,Computer Science ept.,University of California,Santa Barbara,1992
[31]M.Dacier,Y.Deswarte.The Privilege Graph:an Extension to the Typed Access Matrix Model,in European Symposium in Computer Security(ESORICS'94),(D.Gollman,Ed.),(Brighton,UK),Lecture Notes in Computer Science,875,Springer-Verlag.1994.319-34
[32]M.Dacier.Towards Quantitative Evaluation of Computer Security.PhD thesis, Institut National Polytechnique de Toulouse,1994.12
[33]M.Dacier,Y.Deswarte,M.Kaaniche.Models and Tools for Quantitative Assessment of Operational Security,in 12th International Information Security Conference(IFIP/SEC'96),(S.K.Katsikas and D.Gritzalis,Eds.),(Samos,Greece),Chapman & Hall.1996.177-86
[34]R.Ortalo,Y.Deswarte,M.Kaaniche.Experimenting with quantitative evaluation tools for monitoring operational security,IEEE Transactions On Software Engineering,1999.25(5):633-650
[35]E.Jonsson.A Quantitative Model of the Security Intrusion Process Based on Attacker Behavior,IEEE Transactions on Software Engineering,1997,23(4):235-245
[36]Andrew Rathmell,Richard Overill,Lorenzo Valeri.Information Warfare Attack Assessment System(IWAAS).Information Warfare Seminar,London,1997.10
[37]C.Salter,O.Saydjari,B.Schneier,J.Walner.Toward a Secure System Engineering Methodology.Proceedings Of New Security Paradigms Workshop.New York:ACM Press,1998
[38]B.Schneier.Attack Trees:Modeling Security Threats.Dr.Dobb's Journal.1999.12
[39]R.J.Ellison,A.P.Moore.Trustworthy Refinement Through Intrusion-Aware Design(TRIAD).Technical Report.CMU/SEI-2003-TR-002.ESC-TR-2003-002.2003.3
[40]C.Phillips,L.Swiler.A graph-based system for network vulnerability analysis.In ACM New Security Paradigms Workshop.1998.71-79
[41]S.Jha,O.Sheyener,J.M.Wing.Two formal analyses of attack graphs.Technical Report CMU-CS-02-109,Carnegie Mellon University.2002.2
[42]谭跃进,陈英武,易进先.系统工程原理.长沙:国防科技大学出版社,1999.11
[43]钱学森.一个科学新领域——开放的复杂巨系统及其方法论.城市发展研究.2005.Vol.12 No.5,1-8.转载自:钱学森,于景元,戴汝为.一个科学新领域——开放的复杂巨系统及其方法论.自然杂志.1990.Vol.13 No.1,3-10
[44]黄润生,黄浩编著.混沌及其应用.第二版.武汉:武汉大学出版社,2005.12
[45]何德全.开放的互联网——复杂巨系统.全国互联网应急处理研讨会(IERC of China)'2004
[46]孙东川,魏永斌.因特网系统特性浅析.系统辩证学学报.2005.4.Vo.13 No.2,49-52
[47]何明升.复杂巨系统:互联网——社会研究的一个新视角.学术交流.2005.7.Serial No.136
[48]P.Erdos,A.Renyi.On random graphs.Publications Mathematical,1959,6: 290-297
[49]P.Erdos,A.Renyi.On the evolution of random graphs.Publications of the Mathematical Institute of the Hungarian Academy of Science.1960.5:17-61
[50]P.Erdos,A.Renyi.On the strength of connectedness of a random graph.Acta Mathematica Scientia Hungary.1961,12:261-267
[51]B.B.Mandelbrot.分形对象:形、机遇与维数.文志英,苏虹译.世界图书出版公司,1999.12
[52]B.B.Mandelbrot.大自然的分形几何学.陈守吉译.上海:上海远东出版社,2002
[53]Kenneth J.Falconer.分形几何——数学基础及其应用.曾文曲,刘世耀,戴连贵,高占阳译.沈阳:东北大学出版社,1991.8
[54]Kenneth J.Falconer.分形几何中的技巧.曾文曲,王向阳,陆夷译.沈阳:东北大学出版社,1999.6
[55]Manfred Schroeder.Fractals,Chaos,Power Laws:Minutes from an Infinite Paradise.W.H.Freeman and Company,New York,1991
[56]Ian Stewart.上帝掷骰子吗——混沌之数学.潘涛译.上海:上海远东出版社,1995.10
[57]D.Ruelle,F.Takens.On the Nature of Turbulence.1971.Vol.20,167-192
[58]卢侃,孙建华编译.混沌学传奇.上海:上海翻译出版公司,1991.2
[59]D.J.Watts,S.H.Strogatz.Collective dynamics of small world networks.Nature.1998.393:440-442
[60]A.L.Barabasi,R.Albert.Emergence of scaling in random networks.Science.1999.286:509-512
[61]A.L.Barabasi,R.Albert,H.Jeong.Mean-field theory for scale-free random networks.Physica A,1999.272:173-187
[62]M.Faloutsos,P.Faloutsos,C.Faloutsos.On Power-law Relationships of the Internet Topology.In:Proc.ACM SIGCOMM'99.Comput.Commun.Rev.,1999.251-263
[63]R.Govindan,H.Tangmunarunkit.Heuristics for Internet Map Discovery.In:Proc.IEEE Infocom'2000
[64]G.Caldarelli,R.Marchetti,L.Pietronero.The Fractal Properties of Internet.Europhysics Letters.2000.9.Vol.52 No.4,386-391
[65]Chaoming Song,Shlomo Havlin,Hernan A.Makse.Self-similarity of complex networks.Nature.2005.1.Vol.433.http://www.nature.com/nature
[66]A.Erramilli,R.P.Singh.Application of deterministic chaotic maps to characterize broadband traffic,in Proc.7th ITC Specialists Seminar,Morristown,NJ,1990
[67]A.Erramilli,R.P.Singh,P.Pruthi.An application of deterministic chaotic maps to model packet traffic.Queueing Syst.,1995.Vol.20,171-206
[68]P.Pruthi.An Application of Chaotic Maps to Packet Traffic Modeling.Ph.D.Dissertation,Royal Institute of Technology,KTH,Stockholm,Sweden,1995.10
[69]W.E.Leland,M.S.Yaqqu,W.Willinger,D.V.Wilson.On the Self-Similar Nature of Ethernet Traffic.Proc.SIGCOMM'93,Vol.23,No.4,1993.10
[70]W.E.Leland,M.S.Taqqu,W.Willinger,D.V.Wilson.On the Self-Similar Nature of Ethernet Traffic(Extended Version).IEEE/ACM Transactions On Networking.1994.2.Vol 2,No 1
[71]M.Garett,W.Willinger.Analysis,Modeling and Generation of Self-Similar VBR Video Traffic.Proc.SIGCOMM'94,1994
[72]W.Willinger,M.S.Yaqqu,R.Sherman,D.V.Wilson.Self-Similarity Through High-Variability:Statistical Analysis of Ethernet LAN Traffic at the Source Level.Proc.SIGCOMM'95,1995
[73]J.Beran,R.Sherman,M.S.Taqqu,W.Willinger.Variable-bit rate video traffic and long-range dependence.IEEE Trans Commun,1995.Vol.43,1566-1579
[74]V.Paxson,S.Floyd.Wide area traffic:the failure of passion modeling.IEEE/ACM Trans on Networking.1995.Vol.3,No.3
[75]M.E.Crovella,A.Bestavros.Explaining World Wide Web Trafic Self-Similarity.Technical Report:TR-95-015,Computer Science Department,Boston University,1995
[76]D.E.Duffy,A.A.McIntosh,M.Rosenstein,W.Willinger.Statistical Analysis of CCSN/SS7 Traffic Data from Working Subnetworks.IEEE Journal on Selected Areas in Communications,1994.Vol.12,No.3
[77]A.Popescu.Traffic Self-Similarity.Proc.of the IEEE International Conference on Telecommunications,2001.6
[78]Ashok Erramilli,Matthew Roughan,Darryl Veitch,Walter Willinger.Self-Similar Traffic and Network Dynamics.Proceedings of the IEEE.2002.5.Vol.90,No.5
[79]范红,吴亚非,李京春,马朝斌,李嵩,应力,王宁,江常青,张鉴,赵敬宇.信息安全技术 信息安全风险评估规范(国标报批稿),2006.8
[80]冯登国,张阳,张玉清.信息安全风险评估综述.通信学报,2004.7.Vol.25 No.7,10-18
[81]宣蕾,卢锡城.信息系统安全风险评估研究及在预警中的实现.中国信息协会信息安全专业委员会年会文集,2004.6.298-303
[82]王兴元.复杂非线性系统中的混沌.电子工业出版社,2003.6
[83]James Gleick.混沌:开创新科学.张淑誉译.上海:上海译文出版社,1990
[84]E.N.Lorenz.Deterministic nonperiodic flow.Atmosphere Science[J].1963.20, 130-141
[85]Li T.Y,Yorke J.A.Period Three Implies Chaos,American Mathematical Monthly [J],1975,82,985-992
[86]R.L.Devaney.An Introduction to Redwood city.calif,1989
[87]吕金虎,陆君安、陈士华编著.混沌时间序列分析及其应用.武汉:武汉大学出版社,2002.12
[88]E.A.Spiegel,A.Wolf.Chaos and the solar cycle.In Chaos in Astrophysics,volume 497 of Annals of the New York Academy of Science.1987.55-60
[89]J.Benhabib.Cycle and Chaos in Economic Equilibrium,Princeton University Press,Princeton,1992
[90]E.N.Lorenz.混沌的本质.刘式达译.气象出版社,1997
[91]陈守吉,张立明编著.分形与图象压缩.上海科技教育出版社,1998
[92]关新平.混沌控制及其在保密通信中的应用.国防工业出版社,2002
[93]刘华杰.分形艺术.湖南科学技术出版社,1997.12
[94]Elart von Collani.Large Quantification by Stochastic Models.Kalashnikov Memorial Seminar.2002
[95]Elart Von Collani.Defining the Science of Stochastics.Sigma Series in Stochastics.Heldermann Verlag Lemgo.2004.http://www.heldermann.de/SSS/SSS01/sss01.htm
[96]G.P.Box,G.M.Jenkins,G.C.Reinsel.Time series analysis,forecasting and control.Holden-Day,1976
[97]G.E.P.Box,G.M.Jenkins,G.C.Reinsel.时间序列分析:预测与控制.顾岚译.中国统计出版社,1997.9
[98]陈世权,郭嗣琮编著.模糊预测.贵州科技出版社,1994.9
[99]邓聚龙.灰色系统理论教程.武汉:华中理工大学出版社,1992
[100]翁文波.论预测.中国科学技术协会1988年学术年会,1988
[101]翁文波,张清.天干地支记年与预测.北京:石油工业出版社,1993
[102]翁文波.可公度性.地球物理学报,1981.4.Vol.24,No.2
[103]王志明.当代预测宗师.北京:中国文学出版社王志明,1994
[104]马军海.复杂非线性系统的重构技术.天津大学出版社,2005
[105]J.D.Farmer,J.J.Sidorowich.Predicting chaotic time series.Phys.Rev.1987.59(8),845-848
[106]A.S.Lapedes,R.Farber,Nonlinear signal processing using neural networks:prediction and system modeling,Technical Report LA-UR-87-2662,Los Alamos National Laboratory.1987
[107]M.Casdagli.Nonlinear prediction of chaotic time-series.Physica D.1989.35,335-356
[108]G.Sugihara,R.M.May.Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series.Nature.1990.Vol.344,No.19,734-741
[109]林振山.长期预报的相空间近邻等距法.大气科学,1992,Vol.16,530-537
[110]林振山.长期预报的相空间分量组合法.气象学报,1993,Vol.51,392-395
[111]马开玉.统计预测技术的回顾和展望.热带气象学报,2003.11,Vol.19,No.4
[112]X.Zhang and J.Hutchinson.Simple architectures on fast machines:practical issues in nonlinear time series prediction.In A.S.Weigend and N.A.Gershen-feld,editors,Time Series Prediction:forecasting the future and understanding the past,Addison Wesley,1994.219-241
[113]J.Kohlmorgen,K.-R.Muller,K.Pawelzik,Improving short-term prediction with competing experts.ICANN'95:Proc.of the Int.Conf.on Artificial Neural Networks,EC2 &Cie,Paris.1995.2:215-220
[114]Tomasz J.Cholewo,Jacek M.Zurada.Sequential Network Construction for Time Series Prediction.In Proceedings of the IEEE International Joint Conference on Neural Networks.Houston,Texas,USA,June 9-12,1997.2034-2039
[115]Haizhon Li,Robert Kozma.A Dynamic Neural Network Method for Time Series Prediction.0-7803-7898-9/03 IEEE.2003.
[116]V.Petridis,A.Kehagias,L.Petrou,A.Bakirtzis,S.Kiartzis and H.Panagiotou.A bayesian multiple models combination method for time series prediction.Journal of Intelligent and Robotic Systems.2001.31:69-89
[117]G.Dangelmayr,S.Gadaleta,D.Hundley,M.Kirby,Time series prediction by estimating Markov probabilities through topology preserving maps.Proc.SPIE Vol.3812,Applications and Science of Neural Networks,Fuzzy Systems,and Evolutionary Computation Ⅱ,Eds.B.Bosacchi and D.B.Fogel and J.C.Bezdek.1999,86-93
[118]X.Wang,P.Whigham,D.Deng,Time-line hidden markov experts and its application in time series prediction.The Information Science Discussion Paper Series Number 2003/03 June 2003 ISSN 1172-6024
[119]Sayan Mukherjee,Edgar Osuna,Federico Girosi,Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines,Proc.of IEEE NNSP'97,24,1997
[120]K.-R.M(u|¨)ller,A.Smola,G.R(a|¨)tsch,B.Sch(o|¨)lkopf,J.Kohlmorgen,V.Vapnik.Predicting Time Series with Support Vector Machines.Proceedings ICANN'97,p.999.Springer Lecture Notes in Computer Science,1997
[121]Stefan R(u|¨)ping.SVM Kernels for Time Series Analysis.Tagungsband der GI-Workshop.Woche LLWA 01,Dortmund,2001.43-50
[122]L.Ralaivola.Dynamical Modeling with Kernels for Nonlinear Time Series Prediction.In Adv.in Neural Information Processing Systems.2004.16
[123]World-wide Competition Within the EUNITE Network,EUNITE Competition Report.http://neuron.tuke.Sk/competition/index.php
[124]I.Rojas,H.Pomares.Soft-computing techniques for time series forecasting,ESANN'2004 proceedings-European Symposium on Artificial Neural Networks,d-side publi.,2004.93-102
[125]CERT/CC网络安全事件报告.http://www.cert.org
[126]H.Hurst.Long Term storage capacity of reservoirs.Transactions of the American Society of Civil Engineers.1951.116:770-799
[127]B.Mandelbrot.Pareto-levy law and the distribution of income.International Economic Review.1960.1960(1):76-106
[128]罗光春.入侵检测若干关键技术与DDoS攻击研究.博士学位论文.电子科技大学 计算机应用技术,2003.12.20
[129]E.E.Peters.分形市场分析——将混沌理论应用到投资与绎济理论上.储海林,殷勤译.北京:经济科学出版社,2002.7
[130]李水根编著.分形.高等教育出版社,2005
[131]B.Qian,K.Rasheed.Hurst Exponent and Financial Market Predictability.in Proc.Financial Engineering and Applications,2004
[132]向渝.IP网络QoS和安全技术研究.博士学位论文.电子科技大学 通信与信息系统,2003.4.1
[133]E.E.Peters.Fractal Market Analysis:Applying Chaos Theory to Investment and Economics.New York:John Wiley &Sons,inc.,1994
[134]梁静溪,陈昭.不同的分组方法对赫斯特指数的影响.中国软科学,2004年第9期,135-139
[135]K.R.Muller,A.J.Smola,G.Ratsch,B.Scholkopf,J.Kohlmorgen,V.Vapnik,Predicting time series with support vector machines,Proceedings of ICANN,1997
[136]The Santa Fe Time Series Competition Data,http://www-psych.stanford.edu/～andreas/Time-Series/SantaFe.html,1994
[137]胡茑庆.转子碰摩非线性行为与故障辨识的研究.博士学位论文.国防科学技术大学,2001.10
[138]G.L.Barenblatt,G.Looss,D.D.Joseph,Nonlinear Dynamics and Turbulence,Pitman Advanced Publishing Program,1983
[139]J.Theiler,S.Eubank,A.Longtin,B.Galdrikian,J.D.Farmer.Testing for nonlinearity in time series:the method of surrogate data.Physica D.1992.58,77-94
[140]Mauricio Barahona,Chi-Sang Poon.Detection of nonlinear dynamics in short,noisy time series.Nature.1996.Vol.381,215-217
[141]N.H.Packard,J.P.Cruchfield,J.D.Farmer,and R.S.Shaw.Geometry from a time series.Phys.Rev.Lett.,1980.9.Vol.45,712-716
[142]F.Takens.Detecting strange attractors in fluid turbulence,in:Dynamical systems and turbulence-Warwick 1980,LNM 898,New York,Springer-Verlag,1981.p366
[143]John F.Gibson,J.D.Farmer,Martin Casdagli,Stephen Eubank.An analytic approach to practical state space reconstruction.Phys.D.1992.6.Vol.57,1-30
[144]M.Casdagli,T.Sauer,J.A.Yorke.Embedology.J.Stat.Phys.1991.65,579-616
[145]E.N.Lorenz.Deterministic nonperiodic flow.Atmosphere Science[J].1963.20,130-141
[146]M.Henon.A two-dimensional mapping with a strange attractor.Commun.Math.Phys.,1976.Vol.50(1).69-77
[147]郝柏林.从抛物线谈起——混沌动力学引论.上海:上海科技教育出版社,1993.9
[148]陈士华,陆君安.混沌动力学初步.武汉水利大学出版社,1998
[149]C.Grebogi,E.Ott,J.A.Yorke.Are three-frequency quasiperiodic orbits to be expected in typical nonlinear dynamical systems? Phys.Rev.Lett.,1983.Vol.51,No.5,339-342
[150]A.Wolf,J.B.Swift,H.L.Swinney,J.A.Vastano.Determining Lyapunov exponents from a time series.Physica 16D.1985.285-317
[151]P.Grassberger,I.Procaccia.Characterization of strange attractor.Phys.Rev.Lett.,1983.50
[152]P.Grassberger,I.Procaccia.Measuring the strangeness of strange attractor.Physica D.1983.9,189-208
[153]K.P.Harikrishnan,R.Misra,G.Ambika,A.K.Kembhavi.A non-subjective approach to the GP algorithm for analyzing noisy time series.Physica D.2006.215,137-145
[154]雷敏,王志中.非线性时间序列的替代数据检验方法研究.电子与信息学报,2001.Vol.23 No.3,248-254
[155]雷敏.非线性时间序列分析及其在动作表面肌电信号中的应用研究.博士学位论文.上海交通大学,2000.6
[156]F.Camastra,A.Vinciarelli.Estimating the Intrinsic Dimension of Data with a Fractal-based Method[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(10):1404-1407
[157]宣蕾,袁宁,刘文星.网络威胁频率混沌时间序列预测.中国计算机学会信息保密专业委员会2006年学术会议论文集,2006.9.317-322
[158]冯大晨,王文明,郭荣光.混沌学理论、方法及其应用.哈尔滨:北方文艺 出版社,2004.12
[159]刘春芬,宣蕾,王正华,李旭峰.基于SVM的网络威胁频率预测算法研究.计算机工程与科学,第29卷第5期,2007.5.11-14
[160]吕金虎,张锁春.加权一阶局域法在电力系统短期负荷预测中的应用.控制理论与应用,2002.10.Vol.19.No.5
[161]易丹辉主编.数据分析与EViews应用.北京:中国统计出版社,2002.10
[162]张世英,陆晓春,李胜朋.时间序列在城市交通预测中的研究.中国科技论文在线,http://www.paper.edu.cn
[163]周雁.中国民航货运量的时间序列模型.成都理工大学学报(自然科学版),2005.8.Vol.32 No.4
[164]刘德富,康春丽.地震预测直面社会的可行性分析.国际地震动态,2002年第11期
[165]翁文波.预测论基础.北京:石油工业出版社,1984
[166]刘波,刘惠,胡华平,黄遵国.计算机漏洞库系统的设计、实现与应用.计算机工程与科学,2004.Vol.26 No.7
[167]Xuan Lei,Xu Xin.Research on a Quantitative Security Risk Assessment Approach in Large-Scale Early Warning System.Grid and Cooperative Computing-GCC 2004 Workshops.Springer,2004.10.LNCS 3252,490-497.(SCI检索号:BBD88)
[168]宣蕾,苏金树,卢锡城.网络扫描权限证书机制研究.计算机工程与科学,2003.4.Vol.25 No.4.23-25
[169]王锐等译.网络最高安全技术指南.北京:机械工业出版社,1998.5
[170]吴志刚等.GB/T 16264.8—2005/ISO/IEC 9594-8:2001,信息技术 开放系统互连 目录 第8部分:公钥和属性证书框架,2005
[171]S.Farrell,R.Housley.An internet Attribute Certificate Profile for Authorization.RFC3281,2002
[172]D.W.Chadwick.The PERMIS X.509 role based privilege management infrastructure.ACM SACMAT.2002
[2]Thomas R.Peltier.Information security risk analysis.Auerbach,2001
[3]翁文波预测科学网.http://www.wengs.com.cn
[4]翁文波.预测学.吕牛顿,张清编.北京:石油工业出版社,1996.11
[5]C.J.Alberts,A.J.Dorofee.OCTAVE~(SM) Method Implementation Guide.v2.0.Pittsburgh,PA:Software Engineering Institute,Carnegie Mellon University,2001
[6]The International Organization for Standardization,Code of Practice for Information Security Management,ISO/IEC 17799:2000,2000.12
[7]BSI/DISC Committee BDD/2,BS7799 Code of Practice for Information Security Management,1999
[8]The International Organization for Standardization,ISO/IEC TR 13335,Information technology-Guidelines for the Management of IT Security(GMITS),1996-2001
[9]The International Organization for Standardization,System Security Engineering Capability Maturity Model(SSE-CMM),ISO/IEC 21827:2002
[10]中国信息安全风险评估论坛.http://www.cisraf.infosec.org.cn
[11]关义章,戴宗坤 主编,罗万伯,周安民,谭兴烈等编著.信息系统安全工程学.北京:电子工业出版社,2002.12
[12]M.S.Lund,I.Hogganvik,F.Seehusen,K.Stolen.The CORAS framework,the CORAS UML profile for security assessment,and the CORAS library of reusable elements.IST-2000-25031.2003.9
[13]J.Aagedal,F.Braber,T.Dimitrakos,B.Gran,D.Raptis,K.Stolen.Model-based Risk Assessment to Improve Enterprise Security.IEEE.Published in the Proceedings of the Fifth International Enterprise Distributed Object Computing Conference(EDOC 2002),2002.9.51-62
[14]T.Dimitrakos,J.Bicarregui,K.Stolen.CORAS-a framework for risk analysis of security critical systems.ERCIM news,2002.number 49,25-26
[15]W.H.Sanders.Stochastic methods for dependability,performability,and security evaluation.Lecture Notes In Computer Science.2004.3099:97
[16]D.A.Hillson,D.T.Hulett.Assessing Risk Probability:Altemative Approaches,In Proc.2004 PMI Global Congress.2004
[17]Y.Chen,C.Jensen.A General Risk Assessment of Security in Pervasive Computing.Technical Report TCD-CS-2003-45,Department of Computer Science,Trinity College Dublin.2003.11
[18]R.C.Wilcox,B.M.Ayyub.Uncertainty Modeling of Data and Uncertainty Propagation for Risk Studies.Proceedings of the Fourth International Symposium on Uncertainty Modeling and Analysis(ISUMA'03),IEEE.2003
[19]Fang Liu,Yong Chen,Kui Dai,Zhiying Wang.Research on Risk Probability Estimating using Fuzzy Clustering for Dynamic Security Assessment.The Tenth International Conference on Rough Sets,Fuzzy Sets,Data Mining,and Granular Computing,Regina,Saskatchewan,Canada,Ivo Duentsch(Eds.):LNCS,Springer-Verlag,2005.9
[20]刘芳.信息系统安全评估理论及其关键技术研究.博士学位论文.国防科学技术大学.2005.4
[21]C&A Systems Security Ltd.COBRA:Introduction to Risk Analysis,http://www.casystems.zetnet.co.uk/risk.htm.2001
[22]List of Risk Analysis,Assessment and Management Tools.Audit Tools.Vol.1,1998.9
[23]Applied Computer Security Associates,The MITRE Corp..Workshop on Information Security System Rating and Ranking.2001.5
[24]Symantec.http://www.Symantec.com
[25]安络公司.http://www.CNNS.net
[26]W.Ozier.Risk assessment and management.In:T.R.Peltier.Information security risk analysis,Auerbach,2001.p223
[27]S.A.Butler.Security Attribute Evaluation Method.CMU-CS-03-132,Submitted in partial fulfillment of the requirements for the Degree of Doctor of Philosophy,School of Computer Science Carnegie Mellon University,Pittsburgh,PA 15213.2003.5
[28]B.D'Ambrosio,Masami Takikawa,Julie Fitzgerald,Daniel Upper,Suzanne Mahoney.Security Situation Assessment and Response Evaluation(SSARE).Proceedings of the DARPA Information Survivability Conference and Exposition (DISCEXII'01),IEEE.2001
[29]M.Ishiguro,Hironobu Suzuki,Ichiro Murase,Hiroyuki Ohno.Internet Threat Detection System Using Bayesian Estimation.FIRST'2004
[30]P.A.Porras.STAT:A State Transition Analysis Tool for Intrusion Detection.Master's Thesis,Computer Science ept.,University of California,Santa Barbara,1992
[31]M.Dacier,Y.Deswarte.The Privilege Graph:an Extension to the Typed Access Matrix Model,in European Symposium in Computer Security(ESORICS'94),(D.Gollman,Ed.),(Brighton,UK),Lecture Notes in Computer Science,875,Springer-Verlag.1994.319-34
[32]M.Dacier.Towards Quantitative Evaluation of Computer Security.PhD thesis, Institut National Polytechnique de Toulouse,1994.12
[33]M.Dacier,Y.Deswarte,M.Kaaniche.Models and Tools for Quantitative Assessment of Operational Security,in 12th International Information Security Conference(IFIP/SEC'96),(S.K.Katsikas and D.Gritzalis,Eds.),(Samos,Greece),Chapman & Hall.1996.177-86
[34]R.Ortalo,Y.Deswarte,M.Kaaniche.Experimenting with quantitative evaluation tools for monitoring operational security,IEEE Transactions On Software Engineering,1999.25(5):633-650
[35]E.Jonsson.A Quantitative Model of the Security Intrusion Process Based on Attacker Behavior,IEEE Transactions on Software Engineering,1997,23(4):235-245
[36]Andrew Rathmell,Richard Overill,Lorenzo Valeri.Information Warfare Attack Assessment System(IWAAS).Information Warfare Seminar,London,1997.10
[37]C.Salter,O.Saydjari,B.Schneier,J.Walner.Toward a Secure System Engineering Methodology.Proceedings Of New Security Paradigms Workshop.New York:ACM Press,1998
[38]B.Schneier.Attack Trees:Modeling Security Threats.Dr.Dobb's Journal.1999.12
[39]R.J.Ellison,A.P.Moore.Trustworthy Refinement Through Intrusion-Aware Design(TRIAD).Technical Report.CMU/SEI-2003-TR-002.ESC-TR-2003-002.2003.3
[40]C.Phillips,L.Swiler.A graph-based system for network vulnerability analysis.In ACM New Security Paradigms Workshop.1998.71-79
[41]S.Jha,O.Sheyener,J.M.Wing.Two formal analyses of attack graphs.Technical Report CMU-CS-02-109,Carnegie Mellon University.2002.2
[42]谭跃进,陈英武,易进先.系统工程原理.长沙:国防科技大学出版社,1999.11
[43]钱学森.一个科学新领域——开放的复杂巨系统及其方法论.城市发展研究.2005.Vol.12 No.5,1-8.转载自:钱学森,于景元,戴汝为.一个科学新领域——开放的复杂巨系统及其方法论.自然杂志.1990.Vol.13 No.1,3-10
[44]黄润生,黄浩编著.混沌及其应用.第二版.武汉:武汉大学出版社,2005.12
[45]何德全.开放的互联网——复杂巨系统.全国互联网应急处理研讨会(IERC of China)'2004
[46]孙东川,魏永斌.因特网系统特性浅析.系统辩证学学报.2005.4.Vo.13 No.2,49-52
[47]何明升.复杂巨系统:互联网——社会研究的一个新视角.学术交流.2005.7.Serial No.136
[48]P.Erdos,A.Renyi.On random graphs.Publications Mathematical,1959,6: 290-297
[49]P.Erdos,A.Renyi.On the evolution of random graphs.Publications of the Mathematical Institute of the Hungarian Academy of Science.1960.5:17-61
[50]P.Erdos,A.Renyi.On the strength of connectedness of a random graph.Acta Mathematica Scientia Hungary.1961,12:261-267
[51]B.B.Mandelbrot.分形对象:形、机遇与维数.文志英,苏虹译.世界图书出版公司,1999.12
[52]B.B.Mandelbrot.大自然的分形几何学.陈守吉译.上海:上海远东出版社,2002
[53]Kenneth J.Falconer.分形几何——数学基础及其应用.曾文曲,刘世耀,戴连贵,高占阳译.沈阳:东北大学出版社,1991.8
[54]Kenneth J.Falconer.分形几何中的技巧.曾文曲,王向阳,陆夷译.沈阳:东北大学出版社,1999.6
[55]Manfred Schroeder.Fractals,Chaos,Power Laws:Minutes from an Infinite Paradise.W.H.Freeman and Company,New York,1991
[56]Ian Stewart.上帝掷骰子吗——混沌之数学.潘涛译.上海:上海远东出版社,1995.10
[57]D.Ruelle,F.Takens.On the Nature of Turbulence.1971.Vol.20,167-192
[58]卢侃,孙建华编译.混沌学传奇.上海:上海翻译出版公司,1991.2
[59]D.J.Watts,S.H.Strogatz.Collective dynamics of small world networks.Nature.1998.393:440-442
[60]A.L.Barabasi,R.Albert.Emergence of scaling in random networks.Science.1999.286:509-512
[61]A.L.Barabasi,R.Albert,H.Jeong.Mean-field theory for scale-free random networks.Physica A,1999.272:173-187
[62]M.Faloutsos,P.Faloutsos,C.Faloutsos.On Power-law Relationships of the Internet Topology.In:Proc.ACM SIGCOMM'99.Comput.Commun.Rev.,1999.251-263
[63]R.Govindan,H.Tangmunarunkit.Heuristics for Internet Map Discovery.In:Proc.IEEE Infocom'2000
[64]G.Caldarelli,R.Marchetti,L.Pietronero.The Fractal Properties of Internet.Europhysics Letters.2000.9.Vol.52 No.4,386-391
[65]Chaoming Song,Shlomo Havlin,Hernan A.Makse.Self-similarity of complex networks.Nature.2005.1.Vol.433.http://www.nature.com/nature
[66]A.Erramilli,R.P.Singh.Application of deterministic chaotic maps to characterize broadband traffic,in Proc.7th ITC Specialists Seminar,Morristown,NJ,1990
[67]A.Erramilli,R.P.Singh,P.Pruthi.An application of deterministic chaotic maps to model packet traffic.Queueing Syst.,1995.Vol.20,171-206
[68]P.Pruthi.An Application of Chaotic Maps to Packet Traffic Modeling.Ph.D.Dissertation,Royal Institute of Technology,KTH,Stockholm,Sweden,1995.10
[69]W.E.Leland,M.S.Yaqqu,W.Willinger,D.V.Wilson.On the Self-Similar Nature of Ethernet Traffic.Proc.SIGCOMM'93,Vol.23,No.4,1993.10
[70]W.E.Leland,M.S.Taqqu,W.Willinger,D.V.Wilson.On the Self-Similar Nature of Ethernet Traffic(Extended Version).IEEE/ACM Transactions On Networking.1994.2.Vol 2,No 1
[71]M.Garett,W.Willinger.Analysis,Modeling and Generation of Self-Similar VBR Video Traffic.Proc.SIGCOMM'94,1994
[72]W.Willinger,M.S.Yaqqu,R.Sherman,D.V.Wilson.Self-Similarity Through High-Variability:Statistical Analysis of Ethernet LAN Traffic at the Source Level.Proc.SIGCOMM'95,1995
[73]J.Beran,R.Sherman,M.S.Taqqu,W.Willinger.Variable-bit rate video traffic and long-range dependence.IEEE Trans Commun,1995.Vol.43,1566-1579
[74]V.Paxson,S.Floyd.Wide area traffic:the failure of passion modeling.IEEE/ACM Trans on Networking.1995.Vol.3,No.3
[75]M.E.Crovella,A.Bestavros.Explaining World Wide Web Trafic Self-Similarity.Technical Report:TR-95-015,Computer Science Department,Boston University,1995
[76]D.E.Duffy,A.A.McIntosh,M.Rosenstein,W.Willinger.Statistical Analysis of CCSN/SS7 Traffic Data from Working Subnetworks.IEEE Journal on Selected Areas in Communications,1994.Vol.12,No.3
[77]A.Popescu.Traffic Self-Similarity.Proc.of the IEEE International Conference on Telecommunications,2001.6
[78]Ashok Erramilli,Matthew Roughan,Darryl Veitch,Walter Willinger.Self-Similar Traffic and Network Dynamics.Proceedings of the IEEE.2002.5.Vol.90,No.5
[79]范红,吴亚非,李京春,马朝斌,李嵩,应力,王宁,江常青,张鉴,赵敬宇.信息安全技术 信息安全风险评估规范(国标报批稿),2006.8
[80]冯登国,张阳,张玉清.信息安全风险评估综述.通信学报,2004.7.Vol.25 No.7,10-18
[81]宣蕾,卢锡城.信息系统安全风险评估研究及在预警中的实现.中国信息协会信息安全专业委员会年会文集,2004.6.298-303
[82]王兴元.复杂非线性系统中的混沌.电子工业出版社,2003.6
[83]James Gleick.混沌:开创新科学.张淑誉译.上海:上海译文出版社,1990
[84]E.N.Lorenz.Deterministic nonperiodic flow.Atmosphere Science[J].1963.20, 130-141
[85]Li T.Y,Yorke J.A.Period Three Implies Chaos,American Mathematical Monthly [J],1975,82,985-992
[86]R.L.Devaney.An Introduction to Redwood city.calif,1989
[87]吕金虎,陆君安、陈士华编著.混沌时间序列分析及其应用.武汉:武汉大学出版社,2002.12
[88]E.A.Spiegel,A.Wolf.Chaos and the solar cycle.In Chaos in Astrophysics,volume 497 of Annals of the New York Academy of Science.1987.55-60
[89]J.Benhabib.Cycle and Chaos in Economic Equilibrium,Princeton University Press,Princeton,1992
[90]E.N.Lorenz.混沌的本质.刘式达译.气象出版社,1997
[91]陈守吉,张立明编著.分形与图象压缩.上海科技教育出版社,1998
[92]关新平.混沌控制及其在保密通信中的应用.国防工业出版社,2002
[93]刘华杰.分形艺术.湖南科学技术出版社,1997.12
[94]Elart von Collani.Large Quantification by Stochastic Models.Kalashnikov Memorial Seminar.2002
[95]Elart Von Collani.Defining the Science of Stochastics.Sigma Series in Stochastics.Heldermann Verlag Lemgo.2004.http://www.heldermann.de/SSS/SSS01/sss01.htm
[96]G.P.Box,G.M.Jenkins,G.C.Reinsel.Time series analysis,forecasting and control.Holden-Day,1976
[97]G.E.P.Box,G.M.Jenkins,G.C.Reinsel.时间序列分析:预测与控制.顾岚译.中国统计出版社,1997.9
[98]陈世权,郭嗣琮编著.模糊预测.贵州科技出版社,1994.9
[99]邓聚龙.灰色系统理论教程.武汉:华中理工大学出版社,1992
[100]翁文波.论预测.中国科学技术协会1988年学术年会,1988
[101]翁文波,张清.天干地支记年与预测.北京:石油工业出版社,1993
[102]翁文波.可公度性.地球物理学报,1981.4.Vol.24,No.2
[103]王志明.当代预测宗师.北京:中国文学出版社王志明,1994
[104]马军海.复杂非线性系统的重构技术.天津大学出版社,2005
[105]J.D.Farmer,J.J.Sidorowich.Predicting chaotic time series.Phys.Rev.1987.59(8),845-848
[106]A.S.Lapedes,R.Farber,Nonlinear signal processing using neural networks:prediction and system modeling,Technical Report LA-UR-87-2662,Los Alamos National Laboratory.1987
[107]M.Casdagli.Nonlinear prediction of chaotic time-series.Physica D.1989.35,335-356
[108]G.Sugihara,R.M.May.Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series.Nature.1990.Vol.344,No.19,734-741
[109]林振山.长期预报的相空间近邻等距法.大气科学,1992,Vol.16,530-537
[110]林振山.长期预报的相空间分量组合法.气象学报,1993,Vol.51,392-395
[111]马开玉.统计预测技术的回顾和展望.热带气象学报,2003.11,Vol.19,No.4
[112]X.Zhang and J.Hutchinson.Simple architectures on fast machines:practical issues in nonlinear time series prediction.In A.S.Weigend and N.A.Gershen-feld,editors,Time Series Prediction:forecasting the future and understanding the past,Addison Wesley,1994.219-241
[113]J.Kohlmorgen,K.-R.Muller,K.Pawelzik,Improving short-term prediction with competing experts.ICANN'95:Proc.of the Int.Conf.on Artificial Neural Networks,EC2 &Cie,Paris.1995.2:215-220
[114]Tomasz J.Cholewo,Jacek M.Zurada.Sequential Network Construction for Time Series Prediction.In Proceedings of the IEEE International Joint Conference on Neural Networks.Houston,Texas,USA,June 9-12,1997.2034-2039
[115]Haizhon Li,Robert Kozma.A Dynamic Neural Network Method for Time Series Prediction.0-7803-7898-9/03 IEEE.2003.
[116]V.Petridis,A.Kehagias,L.Petrou,A.Bakirtzis,S.Kiartzis and H.Panagiotou.A bayesian multiple models combination method for time series prediction.Journal of Intelligent and Robotic Systems.2001.31:69-89
[117]G.Dangelmayr,S.Gadaleta,D.Hundley,M.Kirby,Time series prediction by estimating Markov probabilities through topology preserving maps.Proc.SPIE Vol.3812,Applications and Science of Neural Networks,Fuzzy Systems,and Evolutionary Computation Ⅱ,Eds.B.Bosacchi and D.B.Fogel and J.C.Bezdek.1999,86-93
[118]X.Wang,P.Whigham,D.Deng,Time-line hidden markov experts and its application in time series prediction.The Information Science Discussion Paper Series Number 2003/03 June 2003 ISSN 1172-6024
[119]Sayan Mukherjee,Edgar Osuna,Federico Girosi,Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines,Proc.of IEEE NNSP'97,24,1997
[120]K.-R.M(u|¨)ller,A.Smola,G.R(a|¨)tsch,B.Sch(o|¨)lkopf,J.Kohlmorgen,V.Vapnik.Predicting Time Series with Support Vector Machines.Proceedings ICANN'97,p.999.Springer Lecture Notes in Computer Science,1997
[121]Stefan R(u|¨)ping.SVM Kernels for Time Series Analysis.Tagungsband der GI-Workshop.Woche LLWA 01,Dortmund,2001.43-50
[122]L.Ralaivola.Dynamical Modeling with Kernels for Nonlinear Time Series Prediction.In Adv.in Neural Information Processing Systems.2004.16
[123]World-wide Competition Within the EUNITE Network,EUNITE Competition Report.http://neuron.tuke.Sk/competition/index.php
[124]I.Rojas,H.Pomares.Soft-computing techniques for time series forecasting,ESANN'2004 proceedings-European Symposium on Artificial Neural Networks,d-side publi.,2004.93-102
[125]CERT/CC网络安全事件报告.http://www.cert.org
[126]H.Hurst.Long Term storage capacity of reservoirs.Transactions of the American Society of Civil Engineers.1951.116:770-799
[127]B.Mandelbrot.Pareto-levy law and the distribution of income.International Economic Review.1960.1960(1):76-106
[128]罗光春.入侵检测若干关键技术与DDoS攻击研究.博士学位论文.电子科技大学 计算机应用技术,2003.12.20
[129]E.E.Peters.分形市场分析——将混沌理论应用到投资与绎济理论上.储海林,殷勤译.北京:经济科学出版社,2002.7
[130]李水根编著.分形.高等教育出版社,2005
[131]B.Qian,K.Rasheed.Hurst Exponent and Financial Market Predictability.in Proc.Financial Engineering and Applications,2004
[132]向渝.IP网络QoS和安全技术研究.博士学位论文.电子科技大学 通信与信息系统,2003.4.1
[133]E.E.Peters.Fractal Market Analysis:Applying Chaos Theory to Investment and Economics.New York:John Wiley &Sons,inc.,1994
[134]梁静溪,陈昭.不同的分组方法对赫斯特指数的影响.中国软科学,2004年第9期,135-139
[135]K.R.Muller,A.J.Smola,G.Ratsch,B.Scholkopf,J.Kohlmorgen,V.Vapnik,Predicting time series with support vector machines,Proceedings of ICANN,1997
[136]The Santa Fe Time Series Competition Data,http://www-psych.stanford.edu/～andreas/Time-Series/SantaFe.html,1994
[137]胡茑庆.转子碰摩非线性行为与故障辨识的研究.博士学位论文.国防科学技术大学,2001.10
[138]G.L.Barenblatt,G.Looss,D.D.Joseph,Nonlinear Dynamics and Turbulence,Pitman Advanced Publishing Program,1983
[139]J.Theiler,S.Eubank,A.Longtin,B.Galdrikian,J.D.Farmer.Testing for nonlinearity in time series:the method of surrogate data.Physica D.1992.58,77-94
[140]Mauricio Barahona,Chi-Sang Poon.Detection of nonlinear dynamics in short,noisy time series.Nature.1996.Vol.381,215-217
[141]N.H.Packard,J.P.Cruchfield,J.D.Farmer,and R.S.Shaw.Geometry from a time series.Phys.Rev.Lett.,1980.9.Vol.45,712-716
[142]F.Takens.Detecting strange attractors in fluid turbulence,in:Dynamical systems and turbulence-Warwick 1980,LNM 898,New York,Springer-Verlag,1981.p366
[143]John F.Gibson,J.D.Farmer,Martin Casdagli,Stephen Eubank.An analytic approach to practical state space reconstruction.Phys.D.1992.6.Vol.57,1-30
[144]M.Casdagli,T.Sauer,J.A.Yorke.Embedology.J.Stat.Phys.1991.65,579-616
[145]E.N.Lorenz.Deterministic nonperiodic flow.Atmosphere Science[J].1963.20,130-141
[146]M.Henon.A two-dimensional mapping with a strange attractor.Commun.Math.Phys.,1976.Vol.50(1).69-77
[147]郝柏林.从抛物线谈起——混沌动力学引论.上海:上海科技教育出版社,1993.9
[148]陈士华,陆君安.混沌动力学初步.武汉水利大学出版社,1998
[149]C.Grebogi,E.Ott,J.A.Yorke.Are three-frequency quasiperiodic orbits to be expected in typical nonlinear dynamical systems? Phys.Rev.Lett.,1983.Vol.51,No.5,339-342
[150]A.Wolf,J.B.Swift,H.L.Swinney,J.A.Vastano.Determining Lyapunov exponents from a time series.Physica 16D.1985.285-317
[151]P.Grassberger,I.Procaccia.Characterization of strange attractor.Phys.Rev.Lett.,1983.50
[152]P.Grassberger,I.Procaccia.Measuring the strangeness of strange attractor.Physica D.1983.9,189-208
[153]K.P.Harikrishnan,R.Misra,G.Ambika,A.K.Kembhavi.A non-subjective approach to the GP algorithm for analyzing noisy time series.Physica D.2006.215,137-145
[154]雷敏,王志中.非线性时间序列的替代数据检验方法研究.电子与信息学报,2001.Vol.23 No.3,248-254
[155]雷敏.非线性时间序列分析及其在动作表面肌电信号中的应用研究.博士学位论文.上海交通大学,2000.6
[156]F.Camastra,A.Vinciarelli.Estimating the Intrinsic Dimension of Data with a Fractal-based Method[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(10):1404-1407
[157]宣蕾,袁宁,刘文星.网络威胁频率混沌时间序列预测.中国计算机学会信息保密专业委员会2006年学术会议论文集,2006.9.317-322
[158]冯大晨,王文明,郭荣光.混沌学理论、方法及其应用.哈尔滨:北方文艺 出版社,2004.12
[159]刘春芬,宣蕾,王正华,李旭峰.基于SVM的网络威胁频率预测算法研究.计算机工程与科学,第29卷第5期,2007.5.11-14
[160]吕金虎,张锁春.加权一阶局域法在电力系统短期负荷预测中的应用.控制理论与应用,2002.10.Vol.19.No.5
[161]易丹辉主编.数据分析与EViews应用.北京:中国统计出版社,2002.10
[162]张世英,陆晓春,李胜朋.时间序列在城市交通预测中的研究.中国科技论文在线,http://www.paper.edu.cn
[163]周雁.中国民航货运量的时间序列模型.成都理工大学学报(自然科学版),2005.8.Vol.32 No.4
[164]刘德富,康春丽.地震预测直面社会的可行性分析.国际地震动态,2002年第11期
[165]翁文波.预测论基础.北京:石油工业出版社,1984
[166]刘波,刘惠,胡华平,黄遵国.计算机漏洞库系统的设计、实现与应用.计算机工程与科学,2004.Vol.26 No.7
[167]Xuan Lei,Xu Xin.Research on a Quantitative Security Risk Assessment Approach in Large-Scale Early Warning System.Grid and Cooperative Computing-GCC 2004 Workshops.Springer,2004.10.LNCS 3252,490-497.(SCI检索号:BBD88)
[168]宣蕾,苏金树,卢锡城.网络扫描权限证书机制研究.计算机工程与科学,2003.4.Vol.25 No.4.23-25
[169]王锐等译.网络最高安全技术指南.北京:机械工业出版社,1998.5
[170]吴志刚等.GB/T 16264.8—2005/ISO/IEC 9594-8:2001,信息技术 开放系统互连 目录 第8部分:公钥和属性证书框架,2005
[171]S.Farrell,R.Housley.An internet Attribute Certificate Profile for Authorization.RFC3281,2002
[172]D.W.Chadwick.The PERMIS X.509 role based privilege management infrastructure.ACM SACMAT.2002