期刊文献+

基于DPI的数据内容安全检测算法优化与实现 被引量:1

The Optimization and Implementation of Data Content Security Detection Algorithm based on DPI
下载PDF
导出
摘要 近年来,随着计算机技术的快速发展和互联网的迅猛普及,网络安全问题越来越受到人们的重视。文章首先介绍了DPI(Deep Packet Inspection,深度包检测)技术和几种常见的数据流检测算法,针对传统指纹检测算法的不足,提出了一种改进方案并在仿真平台上予以实现,然后研究了K取值不同时检测率的变化趋势,最后,完成了基于DPI技术的安全检测系统的设计,该系统由数据流监控引擎、Web控制台和安全中心三个模块构成,使得用户能够随时掌握系统的网络安全状况,效果显著。实验结果表明,优化后的指纹检测算法效率得到了大幅度提升并可以适应线上检测的要求,且K取值为16时,检测效果最好。 Recently, with the rapid development of computer technology and Intemet, network security has got more and more attention. DPI technology and several commonly used Data stream Detection Algorithms were introduced and an improved scheme of traditional fingerprint detection algorithm was put forward because of its shortcomings. The improved scheme was implemented on the simulation platform and the trend of detection rate was studied when K has different values. In addition, a security detection system based on DPI technology was designed and the system consists of data stream monitoring engine, Web console and security centre, which enables users keep track of the network security situation and the effect is remarkable. The experiments prove that the optimized fingerprint detection algorithm can enhance the efficiency, adapt to the requirement of online detection and when the K value is 16, the detection effect is best.
出处 《信息网络安全》 2013年第7期2-6,共5页 Netinfo Security
关键词 DPI 指纹检测算法 安全检测系统 DPI fingerprint detection algorithm security detection system
  • 相关文献

参考文献7

二级参考文献28

  • 1温志贤,李小勇.基于支持向量机的网络流量异常检测[J].西北师范大学学报(自然科学版),2005,41(3):27-31. 被引量:6
  • 2安景琦,刘贵全,钱权.一种基于隐Markov模型的异常检测技术[J].计算机应用,2005,25(8):1744-1746. 被引量:3
  • 3黄丽琼,何中市.基于统计语义和结构特征的自动文摘[J].广西师范大学学报(自然科学版),2006,24(4):187-190. 被引量:8
  • 4MOORE A W,PAPAGIANNAKI K.Toward the accurate identification of network applications[C] //Proc.of the 6th International Workshop on Passive and Active Network Measurement.Heidelberg:Springer Verlag,2005:41-54.
  • 5LI Wei,CANINI M,MOORE A W.Efficient application identification and the temporal and spatial stability of classification schema[J].Computer Networks,2009,53(6):790-809.
  • 6CHOI K,CHOI K J.Pattern Matching of Packet Payload for Network Traffic Classification[C] //Proc.of the 1st International Conference on Next Generation Network (NGNCON 2006).Korea:Hyatt Regency Jeju,2006.
  • 7ZANDER S,NGUYEN T,ARMITAGE G.Automated Traffic Classification and Application Identification using Machine Learning[C] //Proc.of the IEEE Conference on Local Computer Networks 30th Anniversary,2005:250-257.
  • 8MOORE A W,ZUEV D.Internet traffic classification using Bayesian analysis techniques[C] //Proc.of ACM SIGMETRICS,New York:ACM Press,2005:50-60.
  • 9ERMAN J,MAHANTI A,ARLITT M,COHEN I.Offline/ realtime traffic classification using semi-supervised learning[J].Performance Evaluation,2007,64(9-12):1194-1213.
  • 10DAI Lei,YUN Xiaochun,XIAO Jun.Optimizing Traffic Classification Using Hybrid Feature Selection[C] //Proc.of the Ninth International Conference on Web-Age Information Management,2008.

共引文献11

同被引文献12

引证文献1

二级引证文献2

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