期刊文献+

基于游程检测与快速傅里叶变换的加密比特流识别 被引量:1

Identification of Encrypted Bit Stream Based on Runs Test and Fast Fourier Transform
下载PDF
导出
摘要 为获得链路层中的加密与未加密比特流样本,首先提出了基于游程检测方法的链路层加密比特流识别方案,解决了未知网络环境下的加密与未加密比特流样本获取问题。同时,采用快速傅里叶变换分别对加密与未加密比特流样本进行处理,根据最大差异原则确定了快速傅里叶变换结果的特征点位置,并基于正态分布原理确定了特征点的取值,建立了特征模板。最后,以某无线网络链路层加密比特流为识别对象,对提出的方案的有效性进行了验证。结果表明,该方案对链路层加密与未加密比特流的识别率均可达到95%以上。 To obtain samples of encrypted data and plaintext in data link layer,an encrypted data identification scheme was provided based on the run test,meanwhile,and the fast Fourier transform was used to process the encrypted data and plaintext.Based on the principle of maximum difference,the characteristic point of the result of the fast Fourier transform was determined.Then the value of the characteristic point and the feature template were determined using the principle of normal distribution.Finally,the identification rate of the proposed scheme was verified,taking a wireless network data as the identification object.The experimental results demonstrate that the rate of the proposed scheme achieves 95% both for the encrypted data and the plaintext.
出处 《计算机科学》 CSCD 北大核心 2015年第1期164-169,共6页 Computer Science
基金 军内科研资助项目(YJJXM12033)资助
关键词 加密比特流 游程检测 快速傅里叶变换 Encrypted bit stream Runs test Fast Fourier transform
  • 相关文献

参考文献11

  • 1龙文,马坤,辛阳,杨义先.适用于协议特征提取的关联规则改进算法[J].电子科技大学学报,2010,39(2):302-305. 被引量:11
  • 2Charles V W, Fabian M, Gerald M M. On inferring application protocol behaviors in encrypted network traffic[J]. Journal of Machine Learning Research, 2006,7 (12) : 2745-2769.
  • 3Sun Guang-lu, Xue Yi-bo, Dong Ying-fei, et al. A Novel Hybrid Method for Effectively Classifying Encrypted Traffic[C]// Pro- ceedings of Communications and Systems Security, 2010, GLO- BECOM 2010. Miami USA,2010 IEEE,2010:1-5.
  • 4Talieh S T, Mostafa A, Fakhri K, et al. Machine Learning-Based Classification of Encrypted Internet Traffic[C]// 8th Interna- tional Conference, MLDM 2012. Berlin, Germany, 2012 : 578-592.
  • 5Zhang Meng, Zhang Hong-li, Zhang/3o. Encrypted Traffic Clas- sification Based on an Improved Clustering Algorithm[C]// In-ternational Conference, ISCTCS 2012. Beijing, China, 2012 : 124- 131.
  • 6Du Ye, Zhang Ru-hui. Design of a method for encrypted P2P traffic identification using K-means algorithm [J]. Telecommu- nication Systems,2013,53(1) : 163-168.
  • 7赵博,郭虹,刘勤让,邬江兴.基于加权累积和检验的加密流量盲识别算法[J].软件学报,2013,24(6):1334-1345. 被引量:41
  • 8MENEZES AJ, VAN OORSCHOT PC, VANSTONE SA.应用密码学手册[M].胡磊,王鹏,译.北京:电子工业出版社,2005.
  • 9NIST FIPS PUB 140-2-2001. Security Requirements for Crypto- graphic Modules[S]. Washington DC, USA: National Institute of Standards and Technology, 2001.
  • 10NIST SP800-22. A Statistical Test Suite for Random and Pseu- dorandom Number Generators for Cryptographie Applications Revision la [S]. Washington DC, USA: National Institute of Standards and Technology,2010.

二级参考文献28

  • 1SEN S, SPATSCHECK O, WANG D. Accurate, scalable in-network identification of P2P traffic using application signatures[C]//WWW 2004: Proceedings of Thirteenth International World Wide Web Conference. New York: ACM Press, 2004: 512-521.
  • 2HAMZA D, SANDRINE V, DAVID R. A markovian signature-based approach to IP traffic classification[C]// MineNet'07: Proceedings of the Third Annual ACM Workshop on Mining Network Data. San Diego: ACM Press, 2007: 29-34.
  • 3HAFFNER P, SEN S, SPATSCHECK O, et al. ACAS: Automated construction of application signatures[C]// Proceedings of ACM SIGCOMM 2005 Workshops: Conference on Computer Communications. Philadelphia: ACM Press, 2005: 197-202.
  • 4HAN Hong, LU Xian-liang. Data mining aided signature discovery in network-based intrusion detection system[J]. ACM SIGOPS Operating Systems Review, 2002, 36(4): 7-13.
  • 5AGRAWAL R, IMIELINSKI T, WAMI A S. Mining association rules between sets of items in large databases[C]//Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data. Washington: ACM Press, 1993:207-216.
  • 6MOORE A, ZUEV W. Intemet traffic classification using Bayesian analysis techniques[C]//SIGMETRICS 2005: Proceedings of International Conference on Measurement and Modeling of Computer Systems. Banff, AB, Canada: ACM Press. 2005: 50-60.
  • 7FANG W, PETERSON L. Inter-AS traffic patterns and their implications[C]//Conference Record of 1999 IEEE Global Telecommunications Conference. Rio de Janeiro: IEEE Press, 1999:1859-1868.
  • 8PITKOW J. Summary of WWW characterizations[J]. World Wide Web, 1999, 2: 3-13.
  • 9ZANDER S, NGUYENL T, ARMITAGEL G. Self-learning IP traffic classification based on statistical flow characteristics[C]//PAM 2005: Proceedings of 6th International Workshop on Passive and Active Network Measurement. Boston: Springer Verlag. 2005: 325-328.
  • 10KRISHNANURTHY B, WANG J. Automated traffic classification for application specific peering[C]//IMW 2002: Proceeds ofACM SIGCOMM Intemet Measurement Workshop. Marseille: ACM Press, 2002:179-180.

共引文献94

同被引文献17

引证文献1

二级引证文献5

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部