对邮件过滤技术发展现状的比较与分析

Comparison and Analysis of Current Spam Filter

下载PDF

导出

摘要对自学习的过滤技术进行比较和分析,包括已经商品化的方法和目前还处于理论研究阶段的方法,尤其介绍基于机器学习的过滤技术的发展现状,重点研究该领域内的一些新兴过滤技术。在综合比较了一系列的过滤技术的优缺点之后,分析结果表明基于规则的方法和贝叶斯方法是最有潜力的过滤技术。 Focus on adaptive spam filters, from commercial implementations to ideas confined to current research papers. Especially introduce the technology based on machine Learning, focusing on some innovative technology in this field. After the comparison and analysis of the different techniques, rule - based and Bayesian filtering appear to be the greatest potential for future spam prevention.

作者张萍韩立娜

机构地区武汉理工大学计算机科学与技术学院

出处《计算机与数字工程》 2008年第4期102-106,共5页 Computer & Digital Engineering

关键词垃圾邮件过滤组合过滤器规则贝叶斯 spam, filter, ensemble filter, rule, bayesian

分类号 TP181 [自动化与计算机技术—控制理论与控制工程]

引文网络
相关文献

参考文献10

1HIDALGO J. Evaluating cost - sensitive unsolicited bulk email categorization [ C ]. In SAC 02: Proceedings of the 2002ACM symposium on Applied computing, ACM- Press, 2002:615 - 620.
2NUTTER R. Software or appliance solution? Network World Fusion [DB/OL]. http://www. nwfusion. com/ columnists/2004/0301nutter. html,2004, 3, 1.
3YOSHIDA K, ADACHI F, WASHIO T et al. Density - based spam detector[ C]. In KDD 04 : Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining, ACM Press, 2004: 486 - 493.
4DAMIANI E, PARABOSCHI S, SAMARATI P et al. P2P -based collaborative spam detection and filtering [C]. In P2P '04: Proceedings of the Fourth International Conference on Peer - to - Peer Computing, IEEE Computer Society, 2004 . 176 - 183.
5DRUCKER H, WU D, VAPNIK V N. Support Vector Machines for Spare Categorization [ J ] , IEEE Transactions on Neural Networks, 1999,9,20 ( 5 ) : 1048 - 1054.
6RIOS G, ZHA H. Exploring support vector machines and random forests for spam detection[ C]. In Conference on Email and Anti - Spam, 2004.
7LUDLOW M. Just 150 ‘spammers' blamed for e - mail woe [ J ]. The Sunday Times, 2002, 11 ( 1 ).
8CHHABRA S, YERAZUNIS W, SIEFKES C. Spare filtering using a Markov random field model with variable weighting schemas [ C ]. Fourth IEEE International Conference on Data Mining. 2004.11 (1 -4) .347 -350.
9CUNNINGHAM P, NOWLAN N, DELANY S et al. A case -based approach to spare filtering that can track concept drift[C]. In ICCBR03 Workshop on Long -Lived CBR Systems, 2003,6.
10CARRERAS X,MARQUEZ L. Boosting trees for anti - spare email filtering [ C ]. In Proceedings of RANLP - 01,4th International Conference on Recent Advances in Natural Language Processing, Tzigov Chark, BG, 2001.

计算机与数字工程

2008年第4期

浏览历史

内容加载中请稍等...

对邮件过滤技术发展现状的比较与分析

参考文献10

相关作者

相关机构

相关主题

浏览历史