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基于Multi-agent的多用户协作垃圾邮件过滤系统的研究 被引量:2

Research of multi-user collaborative E-mail filtering system based on multi-agent
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摘要 基于单客户端的垃圾邮件过滤系统面对技术越来越高明的垃圾邮件发布者已经突现出它的弱点,多Agent技术为垃圾邮件过滤系统的设计提供了新的思路。旨在将Multi-agent技术和多用户协作的思想引入到垃圾邮件过滤系统中,提出了一个分布式的垃圾邮件过滤系统,使各邮件客户端能够互相协作,共享反垃圾邮件信息,从而提高单客户端垃圾邮件过滤的效果和准确率。 The disadvantages of the email filtering system based on the individual client will present when they face to the spammers who have some technologies. The technology of multi-agent provide a new idea for designing of the email filtering system. The idea of multi- agent and collaborative of users are introduced into email filtering system. A distributed email filtering system is designed to make the clients collaborate and communicate each other. This system can improve the accuracy ofemail filtering.
出处 《计算机工程与设计》 CSCD 北大核心 2007年第14期3523-3525,共3页 Computer Engineering and Design
关键词 垃圾邮件 邮件过滤 MULTI-AGENT系统 朴素贝叶斯算法 协作过滤 spam email email filtering multi-agent system navie bayes collaborative filtering
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  • 1[2]Androutsopoulos I, Paliouras G, Michelakis E. Learning to Filter Unsolicited Commercial E-Mail [R]. Technical Report 2004/2, NCSR "Demokritos", 2004.
  • 2[3]McCallum, Andrew Kachites. Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering [EB/OL]. http://www.cs.cmu.edu/~mccallum/bow, 1996.
  • 3[4]Androutsopoulos I, Koutsias J, Chandrinos K V, et al. An evaluation of naive bayesian anti-spam filtering[C]// Potamias G, Moustakis V, Someren Van M, et al. Proceedings of the Workshop on Machine Learning in the New Information Age. Barcelona: 11th European Conference on Machine Learning (ECML 2000), 2000: 9-17.
  • 4[5]Sahami M. Using Machine Learning to Improve Information Access [EB/OL]. http://ai.stanford.edu/~sahami/bio.html, 1998.
  • 5[6]Sahami M, Dumais S, Heckerman D, et al. A bayesian approach to filtering junk e-mail[C]// Sahami Mehran, Craven Mark, Joachims Thorsten, et al. Learning for Text Categorization: Papers from the 1998 Workshop.[s.l.]: AAAI, 1998.
  • 6[7]Friedman N, Geiger D, Goldszmidt M. Bayesian network classifiers [J]. Machine Learning, 1997, 29:131-163.
  • 7J S Balasubramaniyan,J O Garcia-Fernandez,D Isacoff et al.An architecture for intrusion detection using autonomous agents[C].In:Proceedings of the 14th Annual Magnetism Computer Security Applications Conference,Phoenix,USA, 1998-12:13~24
  • 8Xiaoning Wang. A survey of Intrusion Detection System vulnerabilities and the attack approaches[M].Fernando Colon Osorio,2003-06
  • 9W Jansen,P Mell,T Karygiannis et al. Mobile Agents in Intrusion Detection and Response[C].In:the 12th Annual Canadian Information Technology Security Symposium,2002
  • 10Chunsheng Li ,Qingfeng Song,Chengqi Zhang. MA-IDS Architecture for Distributed Intrusion Detection using Mobile Agents[C].In:Proceedings of the 2nd International Conference on Information Technology for Application,http ://charybdis.mit.csu.edu.au/icita/2004/papers.htm#THEME8

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