期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

基于邮件行为异常的垃圾邮件客户端检测 被引量：2

Design and implementation of a behavior based algorithm to detect spam zombie client
下载PDF
导出
摘要 针对僵尸客户端可能存在的垃圾邮件发送行为,从邮件发送的过程和原理入手,提出一种基于邮件行为异常的僵尸客户端检测方法。实验结果显示,正常主机和正在进行垃圾邮件发送的僵尸客户端在进行邮件发送时存在非常显著的差异。 Considering the spamming behavior of some spam bots, this palSer proposes a behavior- based algorithm to detect spare bots. The results show that there is a significant difference between the normal host and ongoing spam zombie client.
作者 邓国强 李芝棠 李冬 李战春
机构地区 华中科技大学计算机科学与技术学院 下一代互联网接入系统国家工程实验室 华中科技大学网络与计算中心
出处 《广西大学学报（自然科学版）》 CAS CSCD 北大核心 2011年第A01期100-104,共5页 Journal of Guangxi University（Natural Science Edition）
基金 湖北省自然科学基金重点项目(2008CDA021) 中央高校基本科研业务费专项(2010QN047)
关键词 垃圾邮件 僵尸网络 邮件行为 botnet spam behavior of spam
分类号 TP393.08 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献5

  • 1JOHN J, MOSHCHUK A, GRIBBLE S, et al. Studying spamming bothers using botlab[ C ]//In Proceedings of the 6th USENIX Symposium on Networked Systems Design and Implementation ,2009.
  • 2XIE Y, YU F, OSIPKOV I, et al. Spamming bothers: signatures and characteristics[ C]//In Proceedings of ACM SIGCOMM ,2008 : 171-182.
  • 3ANDREAS P, KIRILL L, CHRISTIAN K, et al. Botnet judo: fighting spam with itself[ C ]//In Proceedings of Network and IT Security Conference ,2010.
  • 4MOHAMMED F, AHMED M, MANASRAH, et al. A behavior based algorithm to detect spam bots [ C]//Collaborative Technologies and Systems (CTS) , 2010.
  • 5Network Working Group. RFC2821 [ S]. 2001.

同被引文献20

  • 1COHEN W. Learning rules that classify E-mail[C]. Proc of AAAI Spring symposium on Machine Learning in Information Access, California.. IOS Press, 1996.
  • 2LAI G H, CHOU C W, CHEN C M. Anti-spare filter based on data mining and statistical test[C]. 8th IEEE/ACIS International Conference on Computer and Information Science, Shanghai: Peoples Republic of China, 2009.
  • 3WANG Wenjia. Heterogeneous bayesian ensembles for classifying spam emails[C]. Barcelona: World Con- gress on Computational Intelligence (WCCI 2010), 2010.
  • 4CHIHT, HSUY M, WANSW. Aresearchonu- sing support vector machine to classify chinese spam [C]. Kunming:Sth International Conference on Infor- mation and Management Sciences, 2009.
  • 5ABU Z R, MOHAMMAD A H. Spam detection using genetic assisted artificial immune system[J]. Interna- tional Journal of Pattern Recognition and Artificial In- telligence, 2011,25(8) .. 1275-1295.
  • 6MOHAMMED F AHMED M, MANASRAH, et al. A behavior based algorithm to detect spam Bots[C]. Illinois: Collaborative Technologies and Systems (CTS), 2010.
  • 7NAKSOMBOON S, CHARNSRIPINYO C, WATTA- NAPONGSAKORN N. Considering behavior of send- er in spam mail detection[C]. Gyeongju: 2010 6th In- ternational Conference on Networked Computing (INC), 2010.
  • 8HAYATI P, CHAI K, POTDAR V. Behaviour-based Web spambot detection by utilising action time and ac- tion frequency[C]. Fukuoka: International Conference on Computational Science and its Applications, 2010.
  • 9CORTEZ P, CORREIA A, SOUSA P. Spam email filtering using network-level properties [C]. Berlin: 10th Industrial Conference on Data Mining, 2010.
  • 10FIUMARA G, MARCHI M, PAGANO R. Rule- based spam E-mail annotation[C]. Web Reasoning and Rule Systems, Fourth International Conference, Brix- en~ Springer, 2010.

引证文献2

二级引证文献2

广西大学学报（自然科学版）
2011年 第A01期

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