基于小波变换的信息隐藏与网络流量检测方法研究
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摘要
信息隐藏与网络流量检测分别是信息安全领域中内容安全和运行安全的重要基础技术。论文以小波及其推广—Bandelet变换为主要理论工具,结合经验模式分解和支持向量机方法,研究了二维图像和三维地形的信息隐藏方法、网络流量异常和P2P流量检测方法。论文主要研究内容和贡献如下:
基于新型小波—Bandelet的二维图象信息隐藏方法的研究。传统的数字水印一般通过修改图像数据达到信息隐藏的目的,不适用于保护不允许修改的图像数据,针对这一问题,提出了一种特征级无损数字水印方法。该方法首先对图像实施小波变换,变换后的中高频部分采用Bandelet跟踪图像几何流,将纹理和边缘作为图像的特征构造出图像高频无损水印参数。对于小波变换的低频系数,通过计算矩阵范数,得到低频系数水印参数,从而达到提取图像统计和边缘特征、对图像实现全面保护的目的。实验验证了算法对于各种图像处理攻击和统计攻击有良好的稳健性,能够广泛用于对不可修改数据的保护。
基于小波的三维地形数据信息隐藏方法的研究。研究了基于小波和Hash函数的DEM信息隐藏,提出了多组DEM数据融合伪装传输的算法,该算法可以从隐藏数据中恢复多组真实的DEM数据,大大提高了数据传输效率和安全性;通过改进基于行小波变换及其编码,在保持地形形状和起伏特征的前提下,实现高程数据的极低比特率低存储压缩;结合可嵌入隐藏信息的小波系数集合生成方法与基于视觉系统(HVS)小波域量化噪声的视觉权重(JND)分析技术,提出了自适应确定信息嵌入强度的方法,隐藏过程采用分组密码Rijndael生成单向Hash函数,因此算法高度安全、可以公开;利用小波、方向经验模式分解(DEMD)实施图像的纹理分割,将图像分为纹理(高频)部分和非纹理(低频)部分,从而得到高精度的图像分解,在此基础上提出了基于矩阵偏差度的DEM水印算法,嵌入水印的DEM可以基本保持地形形状和起伏特征,并具有在没有原图的情况下提取水印的能力。
基于小波的网络流量异常检测方法的研究。利用奇异性理论并结合统计学方法,提出了小波方差流量异常检测方法,并对不同的异常流量采用不同的小波函数进行检测,得到检测效果较好的小波函数,该方法简单、实用;采用信息熵来描述网络流量的表征信息,提出了基于小波和信息熵的网络流量异常检测方法,不仅增强了对异常的检测能力,而且方便了流量异常的分类。实验表明,该方法检测效率高、误报率低。
基于小波与支持向量机的P2P网络流量检测方法的研究。提出了基于双重特征的P2P流量检测方法,该方法利用流量和深层数据包两种特征检测P2P流量,提高了检测效率和检测精度,是一个较准确、高效的P2P流量实时识别方法;进一步提出了基于小波基核函数支持向量机的P2P流量检测方法,该方法通过迭代训练提高检测精度,能较好地从网络流量中分离出P2P流量。最后,设计了一种能够进行应用级分类的检测方法,实验表明,该方法不仅效率高,适合于P2P实时流量检测,而且可以通过调整系统参数实现高精度检测。
Information hiding and network traffic detection are important and fundamental techniques of content security and operation security respectively in information security. Mainly based on wavelet and its generalizations-bandelet, coupled with empirical model decomposition and support vector machine, this thesis deals with new algorithms for information hiding of two-dimensional images and three-dimensional landform data, and detection of network traffic anomalies as well as identification of P2P. The main contributions of the thesis are as follows:
The algorithms of information hiding for two-dimensional images via new types of wavelet-bandelet are proposed. The traditional watermark protects the digital image by modifying the image data to hide the information used to authenticate the image copyright. It is not applicable to the protection of images which are not permitted to be changed. Lossless watermarking of characteristic class is proposed. Wavelet transform is first performed for the original image, then transformed for the high and middle frequency part of the image. The geometric flow is traced by bandelet. Texture and edge as the feature parameters of the image are used to construct lossless watermarking of high frequency. For low frequency part of wavelet coefficients, a new watermarking scheme is then proposed by selecting optimal matrix norm. The method is used to protect the image from all-around attacks by drawing the statistical character and the edge of the image. The experimental tests show that the proposed approach is robust to image processing and statistics attacks, and can be widely used to protect data which are not permitted to be modified.
The algorithms for three-dimensional landform data hiding are proposed on the basis of the wavelet theory. The original image is not required in the first algorithm, and varied true DEM data can be reconstructed from the hidden information to greatly improve the efficiency and security of datum transmission. In the second algorithm, a modified line-based wavelet and its coding is performed to keep the terrain figure and hypsography, and the terrain information is compressed into low bit ratio and little memory. A new method is obtained, by which the strength of the embedding data is image-adaptive according to the quantization noise of wavelet by using the wavelet coefficient set which can be embedded into hidden information, coupled with the maximum strength of Just Noticeable Distortion (JND) tolerance of Human Visual System (HVS).The proposed algorithm is highly safe and the algorithm can be made public because the hiding process is achieved by Rijndael code to construct Hash one-way function. For data elevation model, DEM image is decomposed into the texture (high frequency part) and other parts (low frequency part) by combining wavelet and directional empirical model decomposition (DEMD), and the highly precise decomposition of the DEM image is obtained. According to this decomposition, a novel matrix-deviation-based watermarking approach is proposed. The proposed approach is used to keep the terrain figure and hypsography and extract water watermarking from the hidden information without the original image.
Based on the wavelet theory , the algorithms for anomaly detection of network traffic are proposed. First, a method for anomaly detection of network traffic called wavelet variance detection method is proposed, which stems from wavelet singularity theory and statistics. The various wavelet functions are applied to detection so that better wavelet functions can be performed. This method is simple and practical. Moreover, information entropy is used to describe the features of network traffic, and the method for anomaly detection of network traffic based on wavelet and information entropy is proposed. As a result, the detection ability of the anomalies is enhanced, and the classification process becomes more convenient. The experimental results show that the method is efficient and accurate.
Based on the wavelet and support vector machine theories, the algorithms for identification P2P traffic are proposed. Firstly, a P2P traffic identification method based on twofold features is proposed. By the simultaneous use of the traffic and payload features, this method improves the efficiency and accuracy of the identification, and therefore is promising for its application to real-time detection. Secondly, a P2P traffic identification method based on the support vector machine with wavelet-based kernel function is proposed. With this method, an iterative training process is adopted to achieve better accuracy. Lastly, an identification method for application-level classification is proposed. Experimental results show that this method is efficient, suitable for real-time traffic identification, and the identification accuracy can be improved by manipulated parameters.
基于新型小波—Bandelet的二维图象信息隐藏方法的研究。传统的数字水印一般通过修改图像数据达到信息隐藏的目的,不适用于保护不允许修改的图像数据,针对这一问题,提出了一种特征级无损数字水印方法。该方法首先对图像实施小波变换,变换后的中高频部分采用Bandelet跟踪图像几何流,将纹理和边缘作为图像的特征构造出图像高频无损水印参数。对于小波变换的低频系数,通过计算矩阵范数,得到低频系数水印参数,从而达到提取图像统计和边缘特征、对图像实现全面保护的目的。实验验证了算法对于各种图像处理攻击和统计攻击有良好的稳健性,能够广泛用于对不可修改数据的保护。
基于小波的三维地形数据信息隐藏方法的研究。研究了基于小波和Hash函数的DEM信息隐藏,提出了多组DEM数据融合伪装传输的算法,该算法可以从隐藏数据中恢复多组真实的DEM数据,大大提高了数据传输效率和安全性;通过改进基于行小波变换及其编码,在保持地形形状和起伏特征的前提下,实现高程数据的极低比特率低存储压缩;结合可嵌入隐藏信息的小波系数集合生成方法与基于视觉系统(HVS)小波域量化噪声的视觉权重(JND)分析技术,提出了自适应确定信息嵌入强度的方法,隐藏过程采用分组密码Rijndael生成单向Hash函数,因此算法高度安全、可以公开;利用小波、方向经验模式分解(DEMD)实施图像的纹理分割,将图像分为纹理(高频)部分和非纹理(低频)部分,从而得到高精度的图像分解,在此基础上提出了基于矩阵偏差度的DEM水印算法,嵌入水印的DEM可以基本保持地形形状和起伏特征,并具有在没有原图的情况下提取水印的能力。
基于小波的网络流量异常检测方法的研究。利用奇异性理论并结合统计学方法,提出了小波方差流量异常检测方法,并对不同的异常流量采用不同的小波函数进行检测,得到检测效果较好的小波函数,该方法简单、实用;采用信息熵来描述网络流量的表征信息,提出了基于小波和信息熵的网络流量异常检测方法,不仅增强了对异常的检测能力,而且方便了流量异常的分类。实验表明,该方法检测效率高、误报率低。
基于小波与支持向量机的P2P网络流量检测方法的研究。提出了基于双重特征的P2P流量检测方法,该方法利用流量和深层数据包两种特征检测P2P流量,提高了检测效率和检测精度,是一个较准确、高效的P2P流量实时识别方法;进一步提出了基于小波基核函数支持向量机的P2P流量检测方法,该方法通过迭代训练提高检测精度,能较好地从网络流量中分离出P2P流量。最后,设计了一种能够进行应用级分类的检测方法,实验表明,该方法不仅效率高,适合于P2P实时流量检测,而且可以通过调整系统参数实现高精度检测。
Information hiding and network traffic detection are important and fundamental techniques of content security and operation security respectively in information security. Mainly based on wavelet and its generalizations-bandelet, coupled with empirical model decomposition and support vector machine, this thesis deals with new algorithms for information hiding of two-dimensional images and three-dimensional landform data, and detection of network traffic anomalies as well as identification of P2P. The main contributions of the thesis are as follows:
The algorithms of information hiding for two-dimensional images via new types of wavelet-bandelet are proposed. The traditional watermark protects the digital image by modifying the image data to hide the information used to authenticate the image copyright. It is not applicable to the protection of images which are not permitted to be changed. Lossless watermarking of characteristic class is proposed. Wavelet transform is first performed for the original image, then transformed for the high and middle frequency part of the image. The geometric flow is traced by bandelet. Texture and edge as the feature parameters of the image are used to construct lossless watermarking of high frequency. For low frequency part of wavelet coefficients, a new watermarking scheme is then proposed by selecting optimal matrix norm. The method is used to protect the image from all-around attacks by drawing the statistical character and the edge of the image. The experimental tests show that the proposed approach is robust to image processing and statistics attacks, and can be widely used to protect data which are not permitted to be modified.
The algorithms for three-dimensional landform data hiding are proposed on the basis of the wavelet theory. The original image is not required in the first algorithm, and varied true DEM data can be reconstructed from the hidden information to greatly improve the efficiency and security of datum transmission. In the second algorithm, a modified line-based wavelet and its coding is performed to keep the terrain figure and hypsography, and the terrain information is compressed into low bit ratio and little memory. A new method is obtained, by which the strength of the embedding data is image-adaptive according to the quantization noise of wavelet by using the wavelet coefficient set which can be embedded into hidden information, coupled with the maximum strength of Just Noticeable Distortion (JND) tolerance of Human Visual System (HVS).The proposed algorithm is highly safe and the algorithm can be made public because the hiding process is achieved by Rijndael code to construct Hash one-way function. For data elevation model, DEM image is decomposed into the texture (high frequency part) and other parts (low frequency part) by combining wavelet and directional empirical model decomposition (DEMD), and the highly precise decomposition of the DEM image is obtained. According to this decomposition, a novel matrix-deviation-based watermarking approach is proposed. The proposed approach is used to keep the terrain figure and hypsography and extract water watermarking from the hidden information without the original image.
Based on the wavelet theory , the algorithms for anomaly detection of network traffic are proposed. First, a method for anomaly detection of network traffic called wavelet variance detection method is proposed, which stems from wavelet singularity theory and statistics. The various wavelet functions are applied to detection so that better wavelet functions can be performed. This method is simple and practical. Moreover, information entropy is used to describe the features of network traffic, and the method for anomaly detection of network traffic based on wavelet and information entropy is proposed. As a result, the detection ability of the anomalies is enhanced, and the classification process becomes more convenient. The experimental results show that the method is efficient and accurate.
Based on the wavelet and support vector machine theories, the algorithms for identification P2P traffic are proposed. Firstly, a P2P traffic identification method based on twofold features is proposed. By the simultaneous use of the traffic and payload features, this method improves the efficiency and accuracy of the identification, and therefore is promising for its application to real-time detection. Secondly, a P2P traffic identification method based on the support vector machine with wavelet-based kernel function is proposed. With this method, an iterative training process is adopted to achieve better accuracy. Lastly, an identification method for application-level classification is proposed. Experimental results show that this method is efficient, suitable for real-time traffic identification, and the identification accuracy can be improved by manipulated parameters.
引文
[1]方滨兴.关于信息安全属性的可计算性初探.第七届中国信息安全大会,2006.
[2]刘振华,尹萍.信息隐藏技术及其应用.北京:科学出版社,2002.
[3]Schyndel Van R G,Tirkel A Z,Osborne C F.A digital watermark.In:IEEE International Conference on Image Processing,Austin,1994,2:86-90.
[4]候启槟.数字图像安全传输及应用研究:博士学位论文.中国科学院自动化所,2003.
[5]张立和,杨义先.软件水印综述.软件学报,2003,14(2):268-277.
[6]Wagner N.Fingerprinting.Proceedings of the 1983 IEEE Symposium on security and privacy,pp:18-22,1983.
[7]Chor B,Fiat A,and Naor M.Tracing traitors.Proceedings of crypto'94,pp:257-270,1994.
[8]Boneh D,Shaw J.Collusion secure fingerprinting for digital data.In 15th annual International cryptology conference.Santa Barbara,California USA No.963 in Lecture Notes in computer science,pp:452-465,Springer-Verlag,1995.
[9]Pfitzmann B and Schunter M.Asymmetric fingerprinting,in Eurocrypt'96,Saragossa,Spain,No.1070 in Lecture Notes in computer science,pp:452-465,Springer-Verlag,1996.
[10]Pfitzmann B and Waider M.Anonymous fingerprinting,in Eucroypt'97.No.1233,in Lecture Notes in computer science,pp:88-102,Springer-Verlag,1997.
[11]邹柏贤,网络流量异常检测与预测方法研究,中国科学院计算机技术研究所博士学位论文,2003.
[12]Daubechies I,Han B,Ron A,and Shen Z.Framelets..MRA based construction of wavelet frames,Applied Comput.Harmon.Anal.2004,14(1):1-46.
[13]Do M N and Vetterli M.Contourlets,Beyond Wavelets.Welland G Ved,Academic Press,2003.
[14]程正兴.小波分析算法与应用.西安:西安交通大学出版社,1997.
[15]成礼智,王红霞,罗永.小波的理论与应用.北京:科学出版社,2004.
[16]Pennec E L and Mallat S.Image compression with geometrical wavelets.In Proc.Of ICIP'2000.Vancouver,Canada,September,2000:661-664.
[17]Pennec E L and Mallat S.Bandelet representations for image compression,Image Processing,2001 International Conference on Volume 1,7-10 Oct.2001Page(s):12 vol.1.
[18]Pennec E L and Mallat S.Sparse Geometric Image Representations With Bandelets IEEE Trans.Image Processing,2005,14(4):423-438.
[19]孙胜和,陆哲明,牛夏牧等著.数字水印技术及其应用.北京:科学出版社,2002.
[20]任勋益,王汝传,王海艳.基于自相似检测DDOS攻击的小波分析方法.通信学报,2006,27(5):6-11.
[21]洪飞,吴志美.基于小波的多尺度网络流量预测模型.2006,29(1):166-170.
[22]Alarcon-Aquio V and Barria J A.Anomaly detection in communication networks using wavelets.IEEE Proceeding Communication,2001,148(6):355-362.
[23]Barford P,Kline J,and Plonka D.A signal analysis of network traffic anomalies[A].Proceedings of ACM SIGCOMM Internet Measurement Workshop[C].Marseilles,France,2002,71-82.
[24]Soon H S,Cho N I,and Kim Robust J W.watermarking of 3D mesh models,2002 IEEE workshop on multimedia signal processing,2002:332-335.
[25]Cox I J,Kilian J,Leighton T and Shamoon T.Secure spread spectrum watermarking for images,audio and video.In IEEE Int.Conf.Image Processing,1996,3:243-246.
[26]Golub G H and VanLoan C F.Matrix Computations.The Johns Hopkins University Press,1996.
[27]Celik M U,Sharma G,Tekalp A M and Saber E.Lossless authentication watermark LAW.Proc.SPIE,2003,5(2):689-698.
[28]Celik M U,Sharma G,Saber E,and Tekalp A M.Lossless watermarking for image authentication:A new framework and an implementation,IEEE Trans.Image Processing,2006,15(4):1042-1049.
[29]Ni Z C,Shi Y Q,Ansari N and Su W.Reversible Data Hiding.IEEE Trans.Circuits and System for Video Technology,2006,16(3):354-362.
[30]Bao P and Ma X H.Image adaptive watermarking using wavelet domain singular value decomposition.IEEE Trans.Circuits and system for video technology,2005,15(1):96-102.
[31]Wu Y H,Guan X,et al.Robust invisible watermarking of volume data is on the 3D DCT.Computer Graphics International,2001:359-362.
[32]Chu W C.DCT-based image watermarking using subsampling.IEEE Trans,multimedia,2003,5(1):34-38.
[33]罗永,成礼智,吴翊,徐志宏.小波变换结合纠错编码的半透明数字水印.计算机学报,2004,27(11):1533-1539.
[34]Xiong Z Guleryuz O and Orchard M T.A DCT-based embedded image coder.IEEE Trans.Signal Processing Letters,1996,3:289-290.
[35]Kii H,Han S S and Shan J H.The digital watermarking method by using both Patchwork and DCT.IEEE Intern.Confere.Multimedia computing and systems,1999,1:895-899.
[36]Kim I,Han S S and Shin J H.An improved tamper-detection method for digital images.IEEE Intern.Confere.Symposium on industry electronics.2001,1:227-231.
[37]王秋生,孙圣和,郑为民.数字音频信号的脆弱水印算法.计算机学报,2002,5(2):520-525.
[38]Kunder D and Hatzinakos D.Digital watermarking using multiresolution waveletdecomposition.Proceedings of the IEEE Intern.Confer.ASSP,1998,5:2969-2972.
[39]Wang Y,Doherty J F and Van Dyck R E.A wavelet based watermarking algorithm for ownership verification of digital images,IEEE Trans.Image Processing,2002,11(2):77-88.
[40]Abry P and Veitch D.Wavelet analysis of long range dependent traffic.IEEE Transactions on Information Theory,1998,44(1):2-15.
[41]刘旺,姜守达,孙圣和.基于三维DCT变换的体数据鲁棒数字水印嵌入算法.电子学报,2005,33(12):2174-2177.
[42]许文丽,王育民.基于差错控制编码的水印检测.电子学报,2005,33(7):1187-1190.
[43]沃焱,韩国强,张见威,张波.基于小波变换和形态学的图像内容认证方法.通信学报,2005,26(8):9-15.
[44]Goroderski V I,Popyach L J and Samoilov V,et al.SVD-based approach to transparent embedding data into digital images.In..Proceedings of Intern.Workshop on Mathematical methods,Models and Arachitercture for Computer network security.London,Springer Verlag,2001,263-277.
[45]李旭东,张振跃.利用矩阵范数实现的公开水印技术.计算机辅助设计与图形学报,2005,(8):1857-1861.
[46]温泉,孙琰锋,王树勋.零水印的概念与应用.电子学报,2003,31(2):214-216.
[47]杨岳湘,罗永,成礼智等,基于Bandelet与自适应矩阵范数的全频率无损水印方法,计算机研究与发展,2007,44(12).
[48]罗永.基于小波与密码理论的信息隐藏方法研究:博士学位论文.国防科技大学,2004.
[49]罗永,成礼智,徐志宏等.基于带参数整数小波变换的可见数字水印.软件学报,2004,15(2):238-249.
[50]罗永,杨岳湘,成礼智等.DEM数据的信息伪装和信息隐藏技术研究.软件学报,18(3)739-745,2007.
[51]杨岳湘,罗永,成礼智等.基于行小波编码的纹理隐藏三维地形数据算法.国防科技大学学报,28(6):90-94,2006.
[52]晏家豪,马睿,吴一波.互联网业务流量监测技术的应用和设计,中国电子网,2006,http://www.21ic.com/news/html/68/show15814.htm.
[53]Roy A Maxion and Frank E F.A Case Study of Ethernet Anomalies in a Distributed Computing Environment.IEEE Transaction on Reliability,1990,39(4):433-443.
[54]Feather E,Siewiorek D and Maxion R.Fault detection in an Ethemet network using anomaly signature matching.Proceedings of ACM SIGCOMM,2002,23(4):279-288.
[55]Thottan M and Ji C.Adaptive Thresholding for Proactive Network Problem Detection.In:IEEE International Workshop on Systems Management,Newport,Rhode Island,1998,108-116.
[56]Thottan M and Ji C.Proactive Anomaly Detection Using Distributed Intelligent Agents.IEEE Network,1998,12(5):21-27.
[57]Thottan M and Ji C.Fault Prediction at The Network Layer Using Intelligent Agents.In IEEE/IFIP,Integrated Network Management Ⅵ,Boston,USA.,May 1999,745-760.
[58]Thotan M and Ji C.Using Network Fault Predictions to Enable IP Traffic Management.Journal of Network and Systems management,2001,9(3):327-346.
[59]Feldmann A,Greenberg A,Lund C,Reingold N,Rexford J,and True F. Deriving Traffic Demands for Operational IP networks: Methodology and Experience. In IEEE/ACM Transactions on Neworking, 2001.
[60] MongFong H, Chun Wei C, ChinShun C, et al. Identification and analysis of P2P traffic-- an example of bit torrent. Proceedings of International Conference on Innovative Computing, Information and Control (ICICIC2006), Beijing, China,2006, 2: 266-269.
[61] Lijuan Z, Zhitong L , Bin L. P2P traffic identification by TCP flow analysis. Proceedings of International Workshop on Networking, Architecture,and Storages, Shenyang, China. Los Alamitos, CA, USA, IEEE Computer Society, 2006.
[62] Suh K, Figueiredo D R, Kurose J, et al. Characterizing and detecting skype-relayed traffic . Proceedings of IEEE Conference on Computer Communications (INFOCOM 2006) , Barcelona, Spain . 2006: 1-12.
[63] Constantionou F, Mavrommatis P. Identifying known and unknown peer-to-peer traffic. Proceedings of Fifth IEEE International Symposium on Network Computing and Applications, Cambridge, MA, USA. Los Alamitos, CA, USA:IEEE Computer Society, 2006: 93-102.
[64] Karagiannis T, Broido A, Faloutsos M, et al. Transport layer identification of P2P traffic. Proceedings of the 4th ACM SIGCOMM Conference on Internet Measurement, Taormina, Italy. New York, NY, USA: ACM, 2004: 121—134.
[65] Madhukar A , Williamson C . A longitudinal study of P2P traffic classification. Proceedings of 14th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, Monterey, CA, USA. Los Alamitos, CA, USA: IEEE Computer Society,2006: 179-188.
[66] Ohzahata S, Hagiwara Y, Terada M, et al. A traffic identification method and evaluations for a pure P2P application. Proceedings of 2005 Passive and Active Measurement ( PAM'05 ) , Boston , MA , USA . Berlin , Germany :Springer-Verlag , 2005: 55—68.
[67] 周世杰,秦志光,吴春江.对等网络流量检测技术,中兴通讯技术, 2007,总第75期,第5期.
[68] Denning D E. An intrusion detection model. IEEE Transactions on Software Engineering, 1987, SE-13: 222-232.
[69]CarlaT L,Brodley E.Temporal sequence learning and data reduction for anomaly detection.In..Reiter M,ed.Proceedings of the 5th Conference on Computer and Communications Security.New York..ACM Press,1998,150-158.
[70]Lee W,Stolfo S,Kui M.A data mining framework for adaptive intrusion detection.In:IEEE Computer Society,ed.Proceedings of the 1999 IEEE Symposium on Security and Privacy.Oakland,CA:IEEE Press,1999,120-132.
[71]Lunt TF,Tamaru A,Gilham F.A real-time intrusion detection expert system (IDES).Technical Report,Menlo Park,CA..Computer Science Laboratory,SRI International,1992.
[72]饶鲜,董春曦,杨绍全.基于支持向量机的入侵检测系统,软件学报,2003,14(3):463-477.
[73]陈光英,张千里,李星.基于SVM分类机的入侵检测系统,通信学报,2002,23(5):51-56.
[74]单征,刘铁铭,楚蓓蓓.基于网络状态的入侵检测模型,信息工程大学学报2002,3(3):9-14.
[75]程光,龚检,丁伟.基于抽样测量的高速网络实时检测模型.软件学报,2003,14(3):594-599.
[76]谭小彬,王卫平,奚宏生,殷保群.计算机系统入侵检测的隐马尔可夫模型,计算机研究与发展,2003,40(2):245-250.
[77]Wang H,Zhang D,Kang S.Detecting SYN flooding attacks[A].Proceedings of the IEEE Infocom[C].New York,2002,123-132.
[78]邹柏贤.一种网络异常实时检测方法.计算机学报,2003,26(8):940-948.
[79]薛丽军,第文军,蒋世奇.一种新的网络流量异常检测方法,燃气涡轮试验与研究,2003,16(3):45-49.
[80]第文军,薛丽军,蒋士奇.运用网络流量自相似分析的网络流量异常检测,兵工自动化,2003,22(6):28-31.
[81]Barford P,and Plonka D.Characteristics of Network Traffic Flow Anomalies,In Proceedings of ACMSIGCOMM Internet Measurement Workshop(IMW),2001.
[82]Lakhina A,Crovella M,Diot C.Diagnosing Network-Wide Traffic Anomalies,In ACM SIGCOMM,Portland,August 2004.
[83]Lakhina A,Crovella M,Diot C.Characterization of Network-Wide Anomalies in Traffic Flows,Technical Report BUCS-2004-020,Boston University,2004.
[84]Lakhina A,Papagiannaki K,Crovella M,Diot C,Kolaczyk ED,Taft N.Structural Analysis of Network Traffic Flows.In ACM SIGMETRICS,2004.
[85]郑军,胡铭曾,云晓春.基于数据流方法的大规模网络异常发现.通信学报,2006,27(2):1-8.
[86]Weil R,Garcia O A,and Wooton J.Detection of traffic anomalies using fuzzy logic based techniques.Fuzzy Systems Proceedings,1998,2:1176-1181.
[87]Matthew R,Subhabrata S,Oliver S,Nick D.Class-of-service mapping for qos:a statistical signature-based approach to ip traffic classification.In IMC '04:Proceedings of the 4th ACM SIGCOMM conference on Internet measurement,pages 135-148,New York,NY,USA,2004,ACMPress.
[88]Bleul H,Rathgeb E P,A Simple.Efficient and Flexible Approach to Measure Multi-Protocol Peer-To-Peer Traffic.IEEE International Conference on Networking(ICN'05) 2005.
[89]Kim M S,Kang H J,Hong J W.Towards Peer-to-Peer Traffic Analysis Using Flows.Lecture Notesin Computer Science,Springer,Heidelberg,Germany,2003,55-67.
[90]Karagiannis T,Broido A,and Faloutsos M.Transport Layer Identification of P2P Traffic.Proceedings of the 4th ACM SIGCOMM conference on Internet measurement,ACM Press,New York,USA,2004,121-134.
[91]Fivos Constantinou,Panayiotis Mavrommatis,Identifying Known and Unknown Peer-to-Peer Traffic,2005.
[92]Dedinski I,De Meer H,Han L,Mathy L,Pezaros D P,Sventek J S,Xiaoying Z.Cross-Layer Peer-to-Peer Traffic Identification and Optimization Based on Active Networking.In Proceedings of the Seventh Annual International Working Conference on Active and Programmable Networks(IWAN'05),CICA,Sophia Antipolis,French Riviera,La Cote d'Azur,France,November 21-23,2005.
[93]邓乃扬,田英杰,数据挖掘中的新方法-支持向量机.北京:科学出版社,2004.
[94]Baudat G and Anouar F.Feature Vector Selection and Projection Using Kernels.Neurocomputing,2003,55(9):21-38.
[95]Tsochantaridis I,Hofmann T,Joachims T,and Altun Y.Support Vector Learning for Interdependent and Structured Output Spaces,ICML,2004.
[96]Chih-Wei Hsu,Chih-Chung Chang,and ChihJen Lin.A practical guide to support vector classification,National Taiwan University,2007.
[97]Cormick,S T.Optimal approximation of sparse hessians and its equivalence to a graph coloring problem.Math.Programming 26(1983) 153-171.
[98]Vapnik V著,许建华,张学工译.统计学习理论[M],北京:电子工业出版社,2004.
[99]Shawe-Taylor J and Cristianini N.Kernel Methods for Pattern Analysis.UK:Cambridge University Press,2004.
[100]Vapnik V.The Nature of Statistical Learning Theory.New York,NY:Springer-Verlag,1995.
[101]黄勇,郑春颖,宋忠虎.多类支持向量机算法综述.计算技术与自动化,2005,24(4):61-63。
[102]Campbell C.Kernel Methods:A Survey of Current Techniques.Neurocomputing,2002,48(1-4):63-84.
[103]Lin K M and Lin C J.A Study on Reduced Support Vector Machines.IEEE Trans.on Neural Networks,2003,14:1449-1459.
[104]Wahba G.Support Vector Machines,Reproducing Kernel Hilbert Spaces and Randomized GACV[A].In Sch(o｜¨)lkopf B,Burges C J C,and Smola A J (Eds).Advances in Kernel Methods - Support Vector Learning.Cambridge,MA:MIT Press 1999:69-88.
[105]Keerthi S S and Lin C J.Asymptotic behaviors of support vector machines with Gaussiankernel,Neural Computation,2003,15(7):1667-1689.
[106]Lin H T and Lin C J.A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods.Technical report,Department of Computer Science and Information Engineering,National Taiwan University,2003.
[107]YueXiang Y,Yong L and Lizhi C.Bandelet Union Optimal Matrix Norms Constructing Lossless Watermarking,6th IEEE International Conference on Computer Information Systems and Industrial Management Applications,IEEE Computer Societ,2007,294-298.
[108]Celik M U,Sharman,Tekalp A M and Saber E.Lossless generalized-LSB data embedding,IEEE Trans.Image Processing,2005,14(2):253-266.
[109]Jian J.Reversible data embedding using a difference expansion,IEEE Trans.Circuits System,Video Technology,2003,13(8):890-896.
[110]Fridrich J,Goljan M and Du R.Invertible authentication,Proc.,SPIE, 2001,4314:197-208.
[111]Lee D T and Schachter B J.Tow Algorithms for Constructing a Delaunay Triangulation.Int.J of Computer and Information Science.1980,9(3):219-242.
[112]Rex A Dwyer.A fast Divide-and-Conquer Algorithm for Constructing Delaunay Triangulations,Algorithmica 1987,(2):137-151.
[113]Mark J Ware.A procedure for automatically correcting invalid flat triangles occurring in triangulated contour data.Computers & Geosciences,1998,24(2):141-151.
[114]胡金星,吴焕萍,潘懋.基于格网划分的海量DEM数据生成.计算机辅助设计与图形图像学报,2004,16(1):41-44.
[115]李永荣,张继贤,黄国满,刘明军.用星载合成孔径雷达(SAR)立体影像生成DEM的方法研究.测绘科学,2005,30(6):28-29.
[116]王宏武,董士海.一个与视点相关的动态多分辨率地形模型.计算机辅助设计与图像学学报,2000,12(8):575-579.
[117]谭兵,蒋定华,许素芹.一种顾及地形特征的DEM内插方法.测绘学院学报,2001,18:26-28.
[118]邸元.基于等高线建立DTM中平坦区域的一种处理方法.计算机辅助设计与图形学学报,2000,12(8):566-570.
[119]罗永,成礼智,陈波,吴翊,数字高程模型数据小波压缩算法,国防科技大学学报,2005,27(2):57-62.
[120]罗永,成礼智,吴翊,徐志宏,基于模糊关系的DEM数据信息伪装技术研究,模糊系统与数学,2004,18(3):34-39.
[121]罗永,成礼智,徐志宏.数字高程模型数整数小波水印算法研究.软件学报,2005,16(6):1096-1103.
[122]罗永,成礼智,吴翊,徐志宏.带纹理的三维地形数据信息隐藏技术研究.计算机辅助设计与图形学学报,2005,17(5):889-894.
[123]Admas M D,and Kossentini F.Reversible integer-to-integer wavelet transforms for image compression:Performance evaluation and analysis,IEEE Trans.Image Processing,2000,9(6):1010-1024.
[124]Moffat A,Neal R M and Witten I H.Arithmetic Coding Revisited.ACM Transactions on Information System,1998,16(3):256-294.
[125]Vetterli M and Herley C.Wavelets and filters banks..Theory and design.IEEE Trans.Signal Processing,1992,40(9):2207-2232.
[126]Su C Y and Wu B F.A Low Memory Zerotree Coding for Arbitrarily Shaped Objects.IEEE Transactions on Image Processing,2003,12(3):271-282.
[127]Watson A B and Yang G Y.Visibility of wavelet quantization noise.IEEE Trans Image Processing,1997,6(8):1164-1174.
[128]Said A,William A,and Pearlman.A New,Fast,and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees.IEEE Trans.Circuits and System for Video Technology,JUNE 1996,6(3):243-250.
[129]Merkle R C.One-way hash functions and DES.In Advances Cryptology,CRYPTO'89,Lecture Notes in Computer Science,1989,435:428-466.
[130]Chrysafis C and Ortega.A.A Line-Based,Reduced Memory,Wavelet Image Compression.IEEE Transactions on Image Processing,2000,9(3):378-389.
[131]Chrysafis C and Ortega A.An algorithm for low memory wavelet compression.In:Proc.IEEE Int.Conf.Image Processing.Kobe,Japan,1999,354-358.
[132]ISO/IEC/JTC1/SC20 WG1 N2000,JPEG2000 Part Ⅰ Final Committee Draft,2000.
[133]张雄明.低内存小波变换及其编码.国防科技大学硕士论文,2003.
[134]Huang N E,Shen Z,and Long S N.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis.Proceedings of the Royal Society of London,454(1998):903-1005.
[135]刘中轩.图像的非线性多尺度结构.中国科学院博士论文,2005.
[136]肖志新,杨岳湘等.基于小波分析的网络流量异常检测与实现.计算机科学,2006,33(10):116-118.
[137]杨岳湘,王海龙等.基于信息熵的大规模网络流量异常分类.计算机工程与科学,2007,29(2):40-43.
[138]王海龙,杨岳湘等.基于子空间方法的大规模网络流量异常检测.计算机工程与应用,2007,43(11):153-156.
[139]谢宏,程浩忠,牛东晓.基于信息熵的粗糙集连续属性离散化算法.计算机学报,2005,28(9):1570-1574.
[140]Mallat S and Hwang W L.Singularity detection and processing with wavelets.IEEE Trans.On Information Theory,1992,38(2):617-643.
[141]Mallat S and Zhong S F.Characterizaton of signals from multiscale edges.IEEE Trans.on Pattern Analysis and Machine Intelligence,1992,14(7):710-732.
[142]曹雪.基于小波技术的广域网流量特性分析与流量突变点检测:硕士学位论文.西安交通大学,2003.
[143]向继,高能,荆继武.聚类算法在网络入侵检测中的应用.计算机工程,2003,29(16):48-50.
[144]杨岳湘,王锐等.基于双重特征的P2P流量检测方法.通信学报,2006,27(11):134-139.
[145]Yuexiang Y,Rui W,et al.Solving P2P Traffic Identification Problems Via Optimized Support Vector Machines.2007 IEEE International Conference on Computer Systems and Applications,IEEE Computer Societ,2007,165-171.
[146]Rui W,Yuexiang Y,Yang L.Solving the App-Level Classification Problem of P2P Traffic Via Optimized Support Vector Machines.Sixth International Conference on Intelligent System Design and Applications,IEEE Computer Societ,2006,534-539.
[147]Opfer R.Multiscale Kernels.Advances in Computational Mathematics,2006,25(4):357-380.
[148]Xiaoguang Z,Gao D and Xinggang Z.Robust wavelet support vector machine for regression estimation.IEEE Trans.Multimedia,2005,11(9):35-45.
[149]Michael A and Tariq S.A framework for multiscale and hybrid RKHS-based approximations.IEEE Trans.Signal Processing,2000,48(12):3559-3568.
[150]Sen S,Spatscheck O,and Wang D M.Accurate,Scalable In-Network Identification of P2P Traffic Using Application Signatures.WWW2004,New York,USA,2004.
[151]Frank J.Artificial intelligence and intrusion detection:current and future derections.Proceedings of the 17th National Computer Security Conference,1994.
[152]Joachims T.Estimating the Generalization Performance of a SVM Efficiently.Proceedings of the International Conference on Machine Learning,Morgan Kaufman,2000.
[2]刘振华,尹萍.信息隐藏技术及其应用.北京:科学出版社,2002.
[3]Schyndel Van R G,Tirkel A Z,Osborne C F.A digital watermark.In:IEEE International Conference on Image Processing,Austin,1994,2:86-90.
[4]候启槟.数字图像安全传输及应用研究:博士学位论文.中国科学院自动化所,2003.
[5]张立和,杨义先.软件水印综述.软件学报,2003,14(2):268-277.
[6]Wagner N.Fingerprinting.Proceedings of the 1983 IEEE Symposium on security and privacy,pp:18-22,1983.
[7]Chor B,Fiat A,and Naor M.Tracing traitors.Proceedings of crypto'94,pp:257-270,1994.
[8]Boneh D,Shaw J.Collusion secure fingerprinting for digital data.In 15th annual International cryptology conference.Santa Barbara,California USA No.963 in Lecture Notes in computer science,pp:452-465,Springer-Verlag,1995.
[9]Pfitzmann B and Schunter M.Asymmetric fingerprinting,in Eurocrypt'96,Saragossa,Spain,No.1070 in Lecture Notes in computer science,pp:452-465,Springer-Verlag,1996.
[10]Pfitzmann B and Waider M.Anonymous fingerprinting,in Eucroypt'97.No.1233,in Lecture Notes in computer science,pp:88-102,Springer-Verlag,1997.
[11]邹柏贤,网络流量异常检测与预测方法研究,中国科学院计算机技术研究所博士学位论文,2003.
[12]Daubechies I,Han B,Ron A,and Shen Z.Framelets..MRA based construction of wavelet frames,Applied Comput.Harmon.Anal.2004,14(1):1-46.
[13]Do M N and Vetterli M.Contourlets,Beyond Wavelets.Welland G Ved,Academic Press,2003.
[14]程正兴.小波分析算法与应用.西安:西安交通大学出版社,1997.
[15]成礼智,王红霞,罗永.小波的理论与应用.北京:科学出版社,2004.
[16]Pennec E L and Mallat S.Image compression with geometrical wavelets.In Proc.Of ICIP'2000.Vancouver,Canada,September,2000:661-664.
[17]Pennec E L and Mallat S.Bandelet representations for image compression,Image Processing,2001 International Conference on Volume 1,7-10 Oct.2001Page(s):12 vol.1.
[18]Pennec E L and Mallat S.Sparse Geometric Image Representations With Bandelets IEEE Trans.Image Processing,2005,14(4):423-438.
[19]孙胜和,陆哲明,牛夏牧等著.数字水印技术及其应用.北京:科学出版社,2002.
[20]任勋益,王汝传,王海艳.基于自相似检测DDOS攻击的小波分析方法.通信学报,2006,27(5):6-11.
[21]洪飞,吴志美.基于小波的多尺度网络流量预测模型.2006,29(1):166-170.
[22]Alarcon-Aquio V and Barria J A.Anomaly detection in communication networks using wavelets.IEEE Proceeding Communication,2001,148(6):355-362.
[23]Barford P,Kline J,and Plonka D.A signal analysis of network traffic anomalies[A].Proceedings of ACM SIGCOMM Internet Measurement Workshop[C].Marseilles,France,2002,71-82.
[24]Soon H S,Cho N I,and Kim Robust J W.watermarking of 3D mesh models,2002 IEEE workshop on multimedia signal processing,2002:332-335.
[25]Cox I J,Kilian J,Leighton T and Shamoon T.Secure spread spectrum watermarking for images,audio and video.In IEEE Int.Conf.Image Processing,1996,3:243-246.
[26]Golub G H and VanLoan C F.Matrix Computations.The Johns Hopkins University Press,1996.
[27]Celik M U,Sharma G,Tekalp A M and Saber E.Lossless authentication watermark LAW.Proc.SPIE,2003,5(2):689-698.
[28]Celik M U,Sharma G,Saber E,and Tekalp A M.Lossless watermarking for image authentication:A new framework and an implementation,IEEE Trans.Image Processing,2006,15(4):1042-1049.
[29]Ni Z C,Shi Y Q,Ansari N and Su W.Reversible Data Hiding.IEEE Trans.Circuits and System for Video Technology,2006,16(3):354-362.
[30]Bao P and Ma X H.Image adaptive watermarking using wavelet domain singular value decomposition.IEEE Trans.Circuits and system for video technology,2005,15(1):96-102.
[31]Wu Y H,Guan X,et al.Robust invisible watermarking of volume data is on the 3D DCT.Computer Graphics International,2001:359-362.
[32]Chu W C.DCT-based image watermarking using subsampling.IEEE Trans,multimedia,2003,5(1):34-38.
[33]罗永,成礼智,吴翊,徐志宏.小波变换结合纠错编码的半透明数字水印.计算机学报,2004,27(11):1533-1539.
[34]Xiong Z Guleryuz O and Orchard M T.A DCT-based embedded image coder.IEEE Trans.Signal Processing Letters,1996,3:289-290.
[35]Kii H,Han S S and Shan J H.The digital watermarking method by using both Patchwork and DCT.IEEE Intern.Confere.Multimedia computing and systems,1999,1:895-899.
[36]Kim I,Han S S and Shin J H.An improved tamper-detection method for digital images.IEEE Intern.Confere.Symposium on industry electronics.2001,1:227-231.
[37]王秋生,孙圣和,郑为民.数字音频信号的脆弱水印算法.计算机学报,2002,5(2):520-525.
[38]Kunder D and Hatzinakos D.Digital watermarking using multiresolution waveletdecomposition.Proceedings of the IEEE Intern.Confer.ASSP,1998,5:2969-2972.
[39]Wang Y,Doherty J F and Van Dyck R E.A wavelet based watermarking algorithm for ownership verification of digital images,IEEE Trans.Image Processing,2002,11(2):77-88.
[40]Abry P and Veitch D.Wavelet analysis of long range dependent traffic.IEEE Transactions on Information Theory,1998,44(1):2-15.
[41]刘旺,姜守达,孙圣和.基于三维DCT变换的体数据鲁棒数字水印嵌入算法.电子学报,2005,33(12):2174-2177.
[42]许文丽,王育民.基于差错控制编码的水印检测.电子学报,2005,33(7):1187-1190.
[43]沃焱,韩国强,张见威,张波.基于小波变换和形态学的图像内容认证方法.通信学报,2005,26(8):9-15.
[44]Goroderski V I,Popyach L J and Samoilov V,et al.SVD-based approach to transparent embedding data into digital images.In..Proceedings of Intern.Workshop on Mathematical methods,Models and Arachitercture for Computer network security.London,Springer Verlag,2001,263-277.
[45]李旭东,张振跃.利用矩阵范数实现的公开水印技术.计算机辅助设计与图形学报,2005,(8):1857-1861.
[46]温泉,孙琰锋,王树勋.零水印的概念与应用.电子学报,2003,31(2):214-216.
[47]杨岳湘,罗永,成礼智等,基于Bandelet与自适应矩阵范数的全频率无损水印方法,计算机研究与发展,2007,44(12).
[48]罗永.基于小波与密码理论的信息隐藏方法研究:博士学位论文.国防科技大学,2004.
[49]罗永,成礼智,徐志宏等.基于带参数整数小波变换的可见数字水印.软件学报,2004,15(2):238-249.
[50]罗永,杨岳湘,成礼智等.DEM数据的信息伪装和信息隐藏技术研究.软件学报,18(3)739-745,2007.
[51]杨岳湘,罗永,成礼智等.基于行小波编码的纹理隐藏三维地形数据算法.国防科技大学学报,28(6):90-94,2006.
[52]晏家豪,马睿,吴一波.互联网业务流量监测技术的应用和设计,中国电子网,2006,http://www.21ic.com/news/html/68/show15814.htm.
[53]Roy A Maxion and Frank E F.A Case Study of Ethernet Anomalies in a Distributed Computing Environment.IEEE Transaction on Reliability,1990,39(4):433-443.
[54]Feather E,Siewiorek D and Maxion R.Fault detection in an Ethemet network using anomaly signature matching.Proceedings of ACM SIGCOMM,2002,23(4):279-288.
[55]Thottan M and Ji C.Adaptive Thresholding for Proactive Network Problem Detection.In:IEEE International Workshop on Systems Management,Newport,Rhode Island,1998,108-116.
[56]Thottan M and Ji C.Proactive Anomaly Detection Using Distributed Intelligent Agents.IEEE Network,1998,12(5):21-27.
[57]Thottan M and Ji C.Fault Prediction at The Network Layer Using Intelligent Agents.In IEEE/IFIP,Integrated Network Management Ⅵ,Boston,USA.,May 1999,745-760.
[58]Thotan M and Ji C.Using Network Fault Predictions to Enable IP Traffic Management.Journal of Network and Systems management,2001,9(3):327-346.
[59]Feldmann A,Greenberg A,Lund C,Reingold N,Rexford J,and True F. Deriving Traffic Demands for Operational IP networks: Methodology and Experience. In IEEE/ACM Transactions on Neworking, 2001.
[60] MongFong H, Chun Wei C, ChinShun C, et al. Identification and analysis of P2P traffic-- an example of bit torrent. Proceedings of International Conference on Innovative Computing, Information and Control (ICICIC2006), Beijing, China,2006, 2: 266-269.
[61] Lijuan Z, Zhitong L , Bin L. P2P traffic identification by TCP flow analysis. Proceedings of International Workshop on Networking, Architecture,and Storages, Shenyang, China. Los Alamitos, CA, USA, IEEE Computer Society, 2006.
[62] Suh K, Figueiredo D R, Kurose J, et al. Characterizing and detecting skype-relayed traffic . Proceedings of IEEE Conference on Computer Communications (INFOCOM 2006) , Barcelona, Spain . 2006: 1-12.
[63] Constantionou F, Mavrommatis P. Identifying known and unknown peer-to-peer traffic. Proceedings of Fifth IEEE International Symposium on Network Computing and Applications, Cambridge, MA, USA. Los Alamitos, CA, USA:IEEE Computer Society, 2006: 93-102.
[64] Karagiannis T, Broido A, Faloutsos M, et al. Transport layer identification of P2P traffic. Proceedings of the 4th ACM SIGCOMM Conference on Internet Measurement, Taormina, Italy. New York, NY, USA: ACM, 2004: 121—134.
[65] Madhukar A , Williamson C . A longitudinal study of P2P traffic classification. Proceedings of 14th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, Monterey, CA, USA. Los Alamitos, CA, USA: IEEE Computer Society,2006: 179-188.
[66] Ohzahata S, Hagiwara Y, Terada M, et al. A traffic identification method and evaluations for a pure P2P application. Proceedings of 2005 Passive and Active Measurement ( PAM'05 ) , Boston , MA , USA . Berlin , Germany :Springer-Verlag , 2005: 55—68.
[67] 周世杰,秦志光,吴春江.对等网络流量检测技术,中兴通讯技术, 2007,总第75期,第5期.
[68] Denning D E. An intrusion detection model. IEEE Transactions on Software Engineering, 1987, SE-13: 222-232.
[69]CarlaT L,Brodley E.Temporal sequence learning and data reduction for anomaly detection.In..Reiter M,ed.Proceedings of the 5th Conference on Computer and Communications Security.New York..ACM Press,1998,150-158.
[70]Lee W,Stolfo S,Kui M.A data mining framework for adaptive intrusion detection.In:IEEE Computer Society,ed.Proceedings of the 1999 IEEE Symposium on Security and Privacy.Oakland,CA:IEEE Press,1999,120-132.
[71]Lunt TF,Tamaru A,Gilham F.A real-time intrusion detection expert system (IDES).Technical Report,Menlo Park,CA..Computer Science Laboratory,SRI International,1992.
[72]饶鲜,董春曦,杨绍全.基于支持向量机的入侵检测系统,软件学报,2003,14(3):463-477.
[73]陈光英,张千里,李星.基于SVM分类机的入侵检测系统,通信学报,2002,23(5):51-56.
[74]单征,刘铁铭,楚蓓蓓.基于网络状态的入侵检测模型,信息工程大学学报2002,3(3):9-14.
[75]程光,龚检,丁伟.基于抽样测量的高速网络实时检测模型.软件学报,2003,14(3):594-599.
[76]谭小彬,王卫平,奚宏生,殷保群.计算机系统入侵检测的隐马尔可夫模型,计算机研究与发展,2003,40(2):245-250.
[77]Wang H,Zhang D,Kang S.Detecting SYN flooding attacks[A].Proceedings of the IEEE Infocom[C].New York,2002,123-132.
[78]邹柏贤.一种网络异常实时检测方法.计算机学报,2003,26(8):940-948.
[79]薛丽军,第文军,蒋世奇.一种新的网络流量异常检测方法,燃气涡轮试验与研究,2003,16(3):45-49.
[80]第文军,薛丽军,蒋士奇.运用网络流量自相似分析的网络流量异常检测,兵工自动化,2003,22(6):28-31.
[81]Barford P,and Plonka D.Characteristics of Network Traffic Flow Anomalies,In Proceedings of ACMSIGCOMM Internet Measurement Workshop(IMW),2001.
[82]Lakhina A,Crovella M,Diot C.Diagnosing Network-Wide Traffic Anomalies,In ACM SIGCOMM,Portland,August 2004.
[83]Lakhina A,Crovella M,Diot C.Characterization of Network-Wide Anomalies in Traffic Flows,Technical Report BUCS-2004-020,Boston University,2004.
[84]Lakhina A,Papagiannaki K,Crovella M,Diot C,Kolaczyk ED,Taft N.Structural Analysis of Network Traffic Flows.In ACM SIGMETRICS,2004.
[85]郑军,胡铭曾,云晓春.基于数据流方法的大规模网络异常发现.通信学报,2006,27(2):1-8.
[86]Weil R,Garcia O A,and Wooton J.Detection of traffic anomalies using fuzzy logic based techniques.Fuzzy Systems Proceedings,1998,2:1176-1181.
[87]Matthew R,Subhabrata S,Oliver S,Nick D.Class-of-service mapping for qos:a statistical signature-based approach to ip traffic classification.In IMC '04:Proceedings of the 4th ACM SIGCOMM conference on Internet measurement,pages 135-148,New York,NY,USA,2004,ACMPress.
[88]Bleul H,Rathgeb E P,A Simple.Efficient and Flexible Approach to Measure Multi-Protocol Peer-To-Peer Traffic.IEEE International Conference on Networking(ICN'05) 2005.
[89]Kim M S,Kang H J,Hong J W.Towards Peer-to-Peer Traffic Analysis Using Flows.Lecture Notesin Computer Science,Springer,Heidelberg,Germany,2003,55-67.
[90]Karagiannis T,Broido A,and Faloutsos M.Transport Layer Identification of P2P Traffic.Proceedings of the 4th ACM SIGCOMM conference on Internet measurement,ACM Press,New York,USA,2004,121-134.
[91]Fivos Constantinou,Panayiotis Mavrommatis,Identifying Known and Unknown Peer-to-Peer Traffic,2005.
[92]Dedinski I,De Meer H,Han L,Mathy L,Pezaros D P,Sventek J S,Xiaoying Z.Cross-Layer Peer-to-Peer Traffic Identification and Optimization Based on Active Networking.In Proceedings of the Seventh Annual International Working Conference on Active and Programmable Networks(IWAN'05),CICA,Sophia Antipolis,French Riviera,La Cote d'Azur,France,November 21-23,2005.
[93]邓乃扬,田英杰,数据挖掘中的新方法-支持向量机.北京:科学出版社,2004.
[94]Baudat G and Anouar F.Feature Vector Selection and Projection Using Kernels.Neurocomputing,2003,55(9):21-38.
[95]Tsochantaridis I,Hofmann T,Joachims T,and Altun Y.Support Vector Learning for Interdependent and Structured Output Spaces,ICML,2004.
[96]Chih-Wei Hsu,Chih-Chung Chang,and ChihJen Lin.A practical guide to support vector classification,National Taiwan University,2007.
[97]Cormick,S T.Optimal approximation of sparse hessians and its equivalence to a graph coloring problem.Math.Programming 26(1983) 153-171.
[98]Vapnik V著,许建华,张学工译.统计学习理论[M],北京:电子工业出版社,2004.
[99]Shawe-Taylor J and Cristianini N.Kernel Methods for Pattern Analysis.UK:Cambridge University Press,2004.
[100]Vapnik V.The Nature of Statistical Learning Theory.New York,NY:Springer-Verlag,1995.
[101]黄勇,郑春颖,宋忠虎.多类支持向量机算法综述.计算技术与自动化,2005,24(4):61-63。
[102]Campbell C.Kernel Methods:A Survey of Current Techniques.Neurocomputing,2002,48(1-4):63-84.
[103]Lin K M and Lin C J.A Study on Reduced Support Vector Machines.IEEE Trans.on Neural Networks,2003,14:1449-1459.
[104]Wahba G.Support Vector Machines,Reproducing Kernel Hilbert Spaces and Randomized GACV[A].In Sch(o｜¨)lkopf B,Burges C J C,and Smola A J (Eds).Advances in Kernel Methods - Support Vector Learning.Cambridge,MA:MIT Press 1999:69-88.
[105]Keerthi S S and Lin C J.Asymptotic behaviors of support vector machines with Gaussiankernel,Neural Computation,2003,15(7):1667-1689.
[106]Lin H T and Lin C J.A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods.Technical report,Department of Computer Science and Information Engineering,National Taiwan University,2003.
[107]YueXiang Y,Yong L and Lizhi C.Bandelet Union Optimal Matrix Norms Constructing Lossless Watermarking,6th IEEE International Conference on Computer Information Systems and Industrial Management Applications,IEEE Computer Societ,2007,294-298.
[108]Celik M U,Sharman,Tekalp A M and Saber E.Lossless generalized-LSB data embedding,IEEE Trans.Image Processing,2005,14(2):253-266.
[109]Jian J.Reversible data embedding using a difference expansion,IEEE Trans.Circuits System,Video Technology,2003,13(8):890-896.
[110]Fridrich J,Goljan M and Du R.Invertible authentication,Proc.,SPIE, 2001,4314:197-208.
[111]Lee D T and Schachter B J.Tow Algorithms for Constructing a Delaunay Triangulation.Int.J of Computer and Information Science.1980,9(3):219-242.
[112]Rex A Dwyer.A fast Divide-and-Conquer Algorithm for Constructing Delaunay Triangulations,Algorithmica 1987,(2):137-151.
[113]Mark J Ware.A procedure for automatically correcting invalid flat triangles occurring in triangulated contour data.Computers & Geosciences,1998,24(2):141-151.
[114]胡金星,吴焕萍,潘懋.基于格网划分的海量DEM数据生成.计算机辅助设计与图形图像学报,2004,16(1):41-44.
[115]李永荣,张继贤,黄国满,刘明军.用星载合成孔径雷达(SAR)立体影像生成DEM的方法研究.测绘科学,2005,30(6):28-29.
[116]王宏武,董士海.一个与视点相关的动态多分辨率地形模型.计算机辅助设计与图像学学报,2000,12(8):575-579.
[117]谭兵,蒋定华,许素芹.一种顾及地形特征的DEM内插方法.测绘学院学报,2001,18:26-28.
[118]邸元.基于等高线建立DTM中平坦区域的一种处理方法.计算机辅助设计与图形学学报,2000,12(8):566-570.
[119]罗永,成礼智,陈波,吴翊,数字高程模型数据小波压缩算法,国防科技大学学报,2005,27(2):57-62.
[120]罗永,成礼智,吴翊,徐志宏,基于模糊关系的DEM数据信息伪装技术研究,模糊系统与数学,2004,18(3):34-39.
[121]罗永,成礼智,徐志宏.数字高程模型数整数小波水印算法研究.软件学报,2005,16(6):1096-1103.
[122]罗永,成礼智,吴翊,徐志宏.带纹理的三维地形数据信息隐藏技术研究.计算机辅助设计与图形学学报,2005,17(5):889-894.
[123]Admas M D,and Kossentini F.Reversible integer-to-integer wavelet transforms for image compression:Performance evaluation and analysis,IEEE Trans.Image Processing,2000,9(6):1010-1024.
[124]Moffat A,Neal R M and Witten I H.Arithmetic Coding Revisited.ACM Transactions on Information System,1998,16(3):256-294.
[125]Vetterli M and Herley C.Wavelets and filters banks..Theory and design.IEEE Trans.Signal Processing,1992,40(9):2207-2232.
[126]Su C Y and Wu B F.A Low Memory Zerotree Coding for Arbitrarily Shaped Objects.IEEE Transactions on Image Processing,2003,12(3):271-282.
[127]Watson A B and Yang G Y.Visibility of wavelet quantization noise.IEEE Trans Image Processing,1997,6(8):1164-1174.
[128]Said A,William A,and Pearlman.A New,Fast,and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees.IEEE Trans.Circuits and System for Video Technology,JUNE 1996,6(3):243-250.
[129]Merkle R C.One-way hash functions and DES.In Advances Cryptology,CRYPTO'89,Lecture Notes in Computer Science,1989,435:428-466.
[130]Chrysafis C and Ortega.A.A Line-Based,Reduced Memory,Wavelet Image Compression.IEEE Transactions on Image Processing,2000,9(3):378-389.
[131]Chrysafis C and Ortega A.An algorithm for low memory wavelet compression.In:Proc.IEEE Int.Conf.Image Processing.Kobe,Japan,1999,354-358.
[132]ISO/IEC/JTC1/SC20 WG1 N2000,JPEG2000 Part Ⅰ Final Committee Draft,2000.
[133]张雄明.低内存小波变换及其编码.国防科技大学硕士论文,2003.
[134]Huang N E,Shen Z,and Long S N.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis.Proceedings of the Royal Society of London,454(1998):903-1005.
[135]刘中轩.图像的非线性多尺度结构.中国科学院博士论文,2005.
[136]肖志新,杨岳湘等.基于小波分析的网络流量异常检测与实现.计算机科学,2006,33(10):116-118.
[137]杨岳湘,王海龙等.基于信息熵的大规模网络流量异常分类.计算机工程与科学,2007,29(2):40-43.
[138]王海龙,杨岳湘等.基于子空间方法的大规模网络流量异常检测.计算机工程与应用,2007,43(11):153-156.
[139]谢宏,程浩忠,牛东晓.基于信息熵的粗糙集连续属性离散化算法.计算机学报,2005,28(9):1570-1574.
[140]Mallat S and Hwang W L.Singularity detection and processing with wavelets.IEEE Trans.On Information Theory,1992,38(2):617-643.
[141]Mallat S and Zhong S F.Characterizaton of signals from multiscale edges.IEEE Trans.on Pattern Analysis and Machine Intelligence,1992,14(7):710-732.
[142]曹雪.基于小波技术的广域网流量特性分析与流量突变点检测:硕士学位论文.西安交通大学,2003.
[143]向继,高能,荆继武.聚类算法在网络入侵检测中的应用.计算机工程,2003,29(16):48-50.
[144]杨岳湘,王锐等.基于双重特征的P2P流量检测方法.通信学报,2006,27(11):134-139.
[145]Yuexiang Y,Rui W,et al.Solving P2P Traffic Identification Problems Via Optimized Support Vector Machines.2007 IEEE International Conference on Computer Systems and Applications,IEEE Computer Societ,2007,165-171.
[146]Rui W,Yuexiang Y,Yang L.Solving the App-Level Classification Problem of P2P Traffic Via Optimized Support Vector Machines.Sixth International Conference on Intelligent System Design and Applications,IEEE Computer Societ,2006,534-539.
[147]Opfer R.Multiscale Kernels.Advances in Computational Mathematics,2006,25(4):357-380.
[148]Xiaoguang Z,Gao D and Xinggang Z.Robust wavelet support vector machine for regression estimation.IEEE Trans.Multimedia,2005,11(9):35-45.
[149]Michael A and Tariq S.A framework for multiscale and hybrid RKHS-based approximations.IEEE Trans.Signal Processing,2000,48(12):3559-3568.
[150]Sen S,Spatscheck O,and Wang D M.Accurate,Scalable In-Network Identification of P2P Traffic Using Application Signatures.WWW2004,New York,USA,2004.
[151]Frank J.Artificial intelligence and intrusion detection:current and future derections.Proceedings of the 17th National Computer Security Conference,1994.
[152]Joachims T.Estimating the Generalization Performance of a SVM Efficiently.Proceedings of the International Conference on Machine Learning,Morgan Kaufman,2000.