期刊文献+

基于活跃熵的DoS攻击检测模型 被引量:22

DoS detection model base on alive entropy
在线阅读 下载PDF
导出
摘要 针对日益严重的拒绝服务(DoS)网络攻击行为,提出了一种基于活跃熵的DoS攻击检测模型。该模型通过活跃通信理论将信息熵与网络流会话相关性结合起来,通过分析网络流量活跃熵值的变化实现对DoS攻击行为的检测。实验结果表明:正常网络流量下活跃熵值基本稳定,在发生DoS攻击时网络流量的活跃熵值波动明显;该模型与静态熵检测模型相比,检测结果更准确,同时能够更有效地检测未知的DoS攻击行为。 An alive entropy model is proposed for detecting increasingly serious Denial of Service(DoS) attacks.The model is based on the theory of active communication that combines the information entropy and related sessions of network flow.The model detects DoS attacks through the analysis of the variation of the network flow's alive entropy.Experiment result show that the alive entropy is stable under normal network flow,and when attack occurs it fluctuates obviously.Compared with other methods based on the static entropy model,the proposed model is more accurate and more effective in detecting unknown DoS attack.
出处 《吉林大学学报(工学版)》 EI CAS CSCD 北大核心 2011年第4期1059-1064,共6页 Journal of Jilin University:Engineering and Technology Edition
基金 国家自然科学基金项目(60973136 61073164) 国家发改委下一代互联网业务试商用及设备产业化专项项目(CNGI-09-01-11) 国家科技部国际合作与交流专项项目(2008DFA12140) 吉林省青年基金项目(201101033) 吉林大学科学前沿与交叉学科创新项目(450060445169)
关键词 计算机系统结构 DOS攻击检测 活跃熵 活跃通信 信息熵 computer systems organization DoS attack detection alive entropy alive communication information entropy
作者简介 作者简介:刘衍珩(1958-),男,教授,博士生导师.研究方向:网络安全.E—mail:yhliu@jlu.edu.cn 通信作者:朱建启(1976-),男,博士,讲师.研究方向:网络安全,数字水印.Email:zhujq@jlu.edu.cn
  • 相关文献

参考文献16

  • 1Mirkovic J, Reiher P. A Taxonomy of DDoS attack and DDoS defense mechanisms [J] ACM SIG- COMM Computer Communications Review, 2004, 34(2) : 39-53.
  • 2Lawniczak A T, Wu H, Di Stefan B N. Detection of anomalous packet traffic via entropy[C] // Proceed ings of the 22nd IEEE Canadian Conference on Elec trical and Computer Engineering, Canada, 2009: 137-141.
  • 3Lee W, Xiang D. Information theoretic measures foranomaly detection [C] /// Proceedings of the IEEE Symposium on Security and Privacy, Washington, 2001:130-147.
  • 4Feinstein I., Sehnackenberg D, Balupari R, et al. Statistical approaches to DDoS attack detection and response[C]// Proceedings of the DARPA Informa- tion Survivability Conference and Exposition, Washington, 2003: 303-314.
  • 5Lall A, Sekar V, Xu J,et al. Data streaming algo rithms for estimating entropy of network traffic[J] ACM SIGMETRICS Performance Evaluation Re view, 2006, 34(1): 145-156.
  • 6I.akhina A, Crovella M, Diot C. Mining anomalies using traffic feature distributions[J]. Computer Communication Review, 2005, 35(4): 217-228.
  • 7Li K, Zhou W L, Yu S, et al. Effective DDoS at tacks detection using generalized entropy metrie[C] //Proceedings of the 9th International Conference on Algorithms and Architectures for Parallel Process ing, Taiwan, 2009:266-280.
  • 8Rahmani H, Sahli N, Kammoun F. Joint entropy a nalysis model for DDoS attack detection[C] // Pro ceedings of the 5th International Conference on In formation Assurance and Security, Xi'an, 2009 267-271.
  • 9Nychis G, Sekar V, Andersen D G. An empirical e- valuation of entropy based traffic anomaly detection[C] // Proceedings of the 8th ACM SIGCOMM In- ternet Measurement Conference, Greece, 2008:151-156.
  • 10Sarvotham S, Riedi R, Baraniuk R. Network traffic analysis and modeling at the connection level[C]// Proceedings of the Internet Measurement Work- shop, San Francisco, 2001:99-103.

二级参考文献7

  • 1Claffy K C, Polyzos G C, Braun H W. Application of sampling methodologies to network traffic characterization[J]. ACM SIGCOMM Computer Communication Review, 1993, 23 (4): 194-203.
  • 2Paxson V, Almes G, Mahdavi J, et al. RFC2330 [DB/OL]. [2007-01-05]. http: //www. ietf. org/ rfc/rfc2330.
  • 3Duffield N. Sampling for passive internet measurement: a review[J]. Statistical Science, 2004, 19 (3):472-498.
  • 4Deering S, Hinden R, RFC2460[DB/OL]. [2007- 01-05]. http://www, ietf. org/rfc/rfc2460.
  • 5WIDE 6bone project. Daily trace[DB/OL]. [2006- 12-12]http://tracer. csl. sony. co. jp/mawi.
  • 6Zseby T, Fokus F, Molina M, et al. Sampling and filtering techniques for IP packet selection[DB/OL]. [2006-12-12]. http://www, ietf. org/internet drafts/draft-ietf-psamp-sample-tech- 10. txt.
  • 7程光,龚俭,丁伟.基于分组标识的网络流量抽样测量模型[J].电子学报,2002,30(12A):1986-1990. 被引量:16

共引文献40

同被引文献174

引证文献22

二级引证文献128

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