全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于近邻传播学习的半监督流量分类方法

DOI: 10.3724/SP.J.1004.2013.01100, PP. 1100-1109

Keywords: 流量分类,半监督学习,近邻传播聚类,流形相似度

Full-Text   Cite this paper   Add to My Lib

Abstract:

?准确的流量分类是进行网络管理、安全检测以及应用趋势分析的基础.针对完全监督和无监督分类的缺陷,提出了一种基于近邻传播学习的半监督流量分类方法.通过引入"近邻传播聚类"机制构建分类模型,使得分类器实现过程简单、运行高效.应用"半监督学习"的思想,抽象出少量已标记样本流约束和流形空间先验信息,定义了"流形相似度"的距离测度,既降低了标记流量样本的复杂度,又提高了流量分类器的性能.理论分析和实验结果表明:算法具有较高的分类准确性和较好的凝聚性.

References

[1]  Yang Jia-Hai, Wu Jian-Ping, An Chang-Qing. Internet Measurement Theory and Its Applications. Beijing: Post & Telecom Press, 2009. 383-408 (杨家海, 吴建平, 安常青. 互联网络测量理论与应用. 北京: 人民邮电出版社, 2009. 383-408)
[2]  Moore A W, Papagiannaki K. Toward the accurate identification of network applications. In: Proceedings of the 2005 Passive and Active Network Measurement. Boston, MA: Springer, 2005: 41-54
[3]  Santos A, Fernandes S, Antonello R, Szabo G, Lopes P, Sadok D. High-performance traffic workload architecture for testing DPI systems. In: Proceedings of the 2011 IEEE Global Telecommunications Conference (GLOBECOM 2011). Houston, TX: IEEE, 2011. 1-5
[4]  Roughan M, Sen S, Spatscheck O, Duffield N. Class-of-service mapping for QoS: a statistical signature-based approach to IP traffic classification. In: Proceedings of the 4th ACM SIGCOMM Internet Measurement Conference. Taormina, Sicily, Italy: ACM, 2004. 135-148
[5]  Erman J, Mahanti A, Arlitt M, Cohen I, Williamson C. Offline/realtime traffic classification using semi-supervised learning. Performance Evaluation, 2007, 64(9-12): 1194-1213
[6]  Zhang J, Tuo X G, Yuan Z, Chen H F. Analysis of fMRI data using an integrated principal component analysis and supervised affinity propagation clustering approach. IEEE Transactions on Biomedical Engineering, 2011, 58(11): 3184-3196
[7]  Liu H W. Community detection by affinity propagation with various similarity measures. In: Proceedings of the 4th International Joint Conference on Computational Sciences and Optimization. Yunnan, China: IEEE, 2011. 182-186
[8]  Bilenko M, Basu S, Mooney R J. Integrating constraints and metric learning in semi-supervised clustering. In: Proceedings of the 21st International Conference on Machine Learning. New York, USA: ACM, 2004. 81-88
[9]  Liu Sheng-Lan, Yan De-Qin. A new global embedding algorithm. Acta Automatica Sinica, 2011, 37(7): 828-835 (刘胜蓝, 闫德勤. 一种新的全局嵌入降维算法. 自动化学报, 2011, 37(7): 828-835)
[10]  Yang W K, Sun C Y, Zhang L. A multi-manifold discriminant analysis method for image feature extraction. Pattern Recognition, 2011, 44(8): 1648-1657
[11]  Yan De-Qin, Liu Sheng-Lan, Li Yan-Yan. An embedding dimension reduction algorithm based on sparse analysis. Acta Automatica Sinica, 2011, 37(11): 1306-1312 (闫德勤, 刘胜蓝, 李燕燕. 一种基于稀疏嵌入分析的降维方法. 自动化学报, 2011, 37(11): 1306-1312)
[12]  Mitzenmacher M, Upfal E. Probability and Computing: Randomized Algorithm and Probabilistic Analysis. Cambridge, U.K.: Cambridge University Press, 2005. 44-45
[13]  Karagiannis T, Broido A, Faloutsos M, Claffy K C. Transport layer identification of P2P traffic. In: Proceedings of the 4th ACM SIGCOMM on Internet Measurement. New York, USA: ACM, 2004. 121-134
[14]  Antonello R, Fernandes S, Sadok D, Kelner J. Characterizing signature sets for testing DPI systems. In: Proceedings of the 2011 IEEE GLOBECOM Workshops. Houston, TX: IEEE, 2011. 678-683
[15]  Zander S, Nguyen T, Armitage G. Automated traffic classification and application identification using machine learning. In: Proceedings of the 30th IEEE Conference on Local Computer Networks. Sydney, Australia: IEEE, 2005. 250-257
[16]  Moore A W, Zuev D. Internet traffic classification using Bayesian analysis techniques. In: Proceedings of the 2005 Internet Traffic Classification Using Bayesian Analysis Techniques (SIGMETRICS). Alberta, Canada: ACM, 2005. 50-60
[17]  Frey B J, Dueck D. Clustering by passing messages between data points. Science, 2007, 315(5814): 972-976
[18]  He Y C, Chen Q C, Wang X L, Xu R F, Bai X H, Meng X J. An adaptive affinity propagation document clustering. In: Proceedings of the 7th International Conference on Information and System. Cairo, Egypt: IEEE, 2010. 1-7
[19]  Wagstaf K, Cardie C. Clustering with instance-level constraints. In: Proceedings of the 17th International Conference on Machine Learning. Stanford, USA: Morgan Kaufmann Publishers, 2000. 1103-1110
[20]  Seung H S, Lee D D. The manifold ways of perception. Science, 2000, 290(5500): 2268-2269
[21]  Zhang S W, Lei Y K. Modified locally linear discriminant embedding for plant leaf recognition. Neurocomputing, 2011, 74(14-15): 2284-2290
[22]  Zhang J P, Wang X D, Krger U, Wang F Y. Principal curve algorithms for partitioning high-dimensional data spaces. IEEE Transactions on Neural Networks, 2011, 22(3): 367-380
[23]  Thedoridis S, Koutroumbas K. Pattern Recognition (3rd edition). Beijing: Publishing House of Electronics Industry, 2010. 389-407
[24]  Moore A W. Moore Set [Online], available: http://www.cl. cam.ac.uk/research/srg/netos/nprobe/data/papsers/sigme- trics/index.html. 2012

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133