[1]谭 营,朱元春.反垃圾电子邮件方法研究进展[J].智能系统学报,2010,5(3):189-201.
 TAN Ying,ZHU Yuan-chun.Advances in antispam techniques[J].CAAI Transactions on Intelligent Systems,2010,5(3):189-201.
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反垃圾电子邮件方法研究进展

参考文献/References:
[1]CRANOR L F, LAMACCHIA B A. Spam![J]. Communications of the ACM, 1998, 41(8): 7483.
[2]GANSTERER W, ILGER M, LECHNER P, et al. Antispam methods—stateoftheart[EB/OL]. [20091105]. http://spam.ani.univie.ac.at/files/FA3840181.pdf.
[3]中国互联网协会反垃圾邮件中心. 2008年第一次中国反垃圾邮件状况调查报告[EB/OL]. [20091105]. http://www.antispam.cn/.
[4]Symantec Inc.. The state of spam, a monthly report—February 2009[EB/OL]. [20091105]. http://eval.symantec.com/mktginfo/enterprise/other_resources/bstate_of_spam_report_022009.enus.pdf.
[5]JENNINGS R. Cost of spam is flattening—our 2009 prediction[EB/OL]. [20091105]. http://www.ferris.com/2009/01/28/costofspamisflatteningour2009predictions/.
[6]Sophos Inc.. Security threat report, July 2009 update: a look at the challenge ahead[EB/OL]. [20091107]. http://www.inuit.se/pub/1214/sophossecuritythreatreportjul2009nawpus.pdf.
[7]中国互联网协会反垃圾邮件中心. 2009年第一季度中国反垃圾邮件状况调查报告[EB/OL]. [20091107]. http://www.antispam.cn/pdf/2009_01_mail_survey.pdf. 
[8]中国互联网协会反垃圾邮件中心. 2008年第四季度中国反垃圾邮件状况调查报告[EB/OL]. [20091107]. http://www.antispam.cn/pdf/2008_4_dc.pdf. 
[9]Wikipedia. KullbackLeibler divergence[EB/OL]. [20091107]. http://en.wikipedia.org/wiki/Information_gain.
[10]KOPRINSKA I, POON J, CLARK J, et al. Learning to classify email[J]. Information Sciences, 2007, 177: 21672187.
[11]YANG Y M, PEDERSEN J O. A comparative study on feature selection in text categorization[C]//Proceedings of International Conference on Machine Learning(ICML’97). San Francisco, USA: Morgan Kaufmann Publishers Inc., 1997: 412420.
[12]GUZELLA T S, CAMINHAS M. A review of machine learning approaches to spam filtering[J]. Expert Systems with Applications, 2009, 36: 1020610222.
[13]BLANZIERI E, BRYL A. A survey of learningbased techniques of email spam filtering[EB/OL]. [20091107]. http://eprints.biblio.unitn.it/archive/00001070/.
[14]ANDROUTSOPOULOS I, PALIOURAS G, MICHELAKIS E. Learning to filter unsolicited commercial email, technique report No. 2004/2[R]. Agia Paraskevi, Greece: NCSR “Demokritos”, 2004.
[15]SCHNEIDER K M. A comparison of event models for naive Bayes antispam email filtering[C]//Proceedings of the 10th Conference of European Chapter of the Association for Computational Linguistics. Morristown, USA: Association for Computational Linguistics, 2003: 307314.
[16]YERAZUNIS W S. Sparse binary polynomial hashing and the CRM114 discriminator[EB/OL]. [20091107]. http://fozzolog.fozzilinymoo.org/images/CRM114_slides.pdf.
[17]SIEFKES C, ASSIS F, CHHABRA S, et al. Combining winnow and orthogonal sparse bigrams for incremental spam filtering[C]//Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases. New York, USA: SpringerVerlag, 2004: 410421.
[18]ODA T, WHITE T. Developing an immunity to spam[J]. Lecture Notes in Computer Science, 2003, 2723: 231242.
[19]RUAN Guangchen, TAN Ying. A threelayer backpropagation neural network for spam detection using artificial immune concentration[J]. Soft Computing, 2010, 14: 139150.
[20]KRASSER S, TANG Y C, GOULD J, et al. Identifying image spam based on header and file properties using C4.5 decision trees and support vector machine learning[C]//Proceedings of IEEE SMC Information Assurance and Security Workshop. New York, USA, 2007: 255261.
[21]NHUNG N P, PHUONG T M. An efficient method for filtering image based spam[J]. Lecture Notes in Computer Science, 2007, 4673: 945953.
[22]YEH C Y, WU C H, DOONG S H. Effective spam classification based on metaheuristics[C]//Proceedings of 2005 IEEE International Conference on Systems, Man, and Cybernetics. Waikoloa, HI, USA, 2005: 38723877.
[23]TASI C H, WU C H. Learning typed behaviors of spam emails using backpropagation neural networks[D]. Kaohsiung, China: ShuTe University, 2004.
[24]WU C H, TSAI C H. A timerobust spam classifier based on backpropagation neural networks and behaviorbased features[C]//Proceedings of the Sixth International Conference on Machine Learning and Cybernetics. Hong Kong, 2007: 1922.
[25]COSTALES B, ALLMAN E. Sendmail[M]. 3rd ed. Sebastopol, USA: O’Reilly & Associates, Inc., 2002.
[26]LIU M, LI Y C, LI W. Spam filtering by stages[C]//Proceedings of 2007 International Conference on Convergence Information Technology. Washington, DC, USA: IEEE Computer Society, 2007: 22092213.
[27]YUE X, ABRAHAM A, CHI Z X, et al. Artificial immune system inspired behaviorbased antispam filter[J]. Soft Computing, 2007, 11: 729740.
[28]GUO Y H, ZHANG Y L, LIU J Y, et al. Research on the comprehensive antispam filter[C]//Proceedings of IEEE International Conference on Industrial Informatics. Singapore, 2006: 10691074.
[29]BHATTACHARYYA M, SCHULTZ M G, ESKIN E, et al. MET: an experimental system for malicious email tracking[C]//Proceedings of the 2002 New Security Paradigms Workshop. Virginia Beach, VA, USA, 2002: 310.
?[30]HERSHKOP S. Behaviorbased email analysis with application to spam detection[D]. New York, USA: Columbia University, 2006.
[31]MARTIN S, SEWANI A, NELSON B, et al. Analyzing behavioral features for email classification[C]//Proceedings of Conference on Email and Anti Spam. Stanford University, USA, 2005.
[32]STOLFO S J, HERSHKOP S, HU C W, et al. Behaviorbased modeling and its application to email analysis[J]. ACM Transactions on Internet Technology, 2006, 6(2): 187221.
[33]BRENDEL R, KRAWCZYK H. Detection methods of dynamic spammers’ behavior[C]//Proceedings of 2nd International Conference on Dependability of Computer Systems. Washington, DC, USA: IEEE Computer Society, 2007: 145152.
[34]RAMACHANDRAN A, FEAMSTER N. Understanding the networklevel behavior of spammers[C]//Proceedings of the 2006 Conference on Applications, Technologies, Architectures,  and Protocols for Computer Communications. New York, USA: ACM, 2006: 291302.
[35]陈建发,吴顺祥. 一种基于用户行为分析的协同反垃圾邮件策略[J]. 电脑知识与技术: 学术交流, 2007(7): 3637.
CHEN Jianfa, WU Shunxiang. An cooperate antispam strategy based on user’s behavioral analysis[J]. Computer Knowledge and Technology: Academic Exchange, 2007(7): 3637.
[36]SPAM LAWS. The CANSPAM Act of 2003 [EB/OL]. [20091107]. http://www.spamlaws.com/federal/index.shtml.
[37]GRIMES G A. Compliance with CANSPAM Act of 2003[J]. Communications of the ACM, 2007, 50: 5562.
[38]Rundfunk and Telekom RegulierungsGmbH. Telekommunikationsgesetz 2003(TKG 2003)[EB/OL]. [20091107]. http://www.rtr.at/de/tk/TKG2003#p107.
[39]HOANCA B. How good are our weapons in the spam wars?[J]. IEEE Technology and Society Magazine, 2006, 25(1): 2230.
[40]HARRIS E. The next step in the spam control war: greylisting[EB/OL]. [20091107]. http://projects.puremagic.com/greylisting/whitepaper.html.
[41]LODER T, ALSTYNE M V, WASH R. An economic answer to unsolicited communication[C]//Proceedings of the 5th ACM Conference on Electronic Commerce. New York, USA: ACM, 2004: 4050.
[42]SAHAMI M, DUMAIS S, HECKERMAN D, et al. A Bayesian approach 〖KG*1/2〗 tofiltering 〖KG*1/2〗 junk〖KG*1/2〗email[C]//Procee dings of the 1998 Workshop on Learning for Text Categorization. Madison, USA, 1998: 5562.
[43]ANDROUTSOPOULOS I, KOUTSIAS J, CHANDRINOS K V, et al. An evaluation of naive Bayesian antispam filtering[C]//Proceedings of the Workshop on Machine Learning in the New Information Age. Barcelona, Spain, 2000: 917.
[44]SHRESTHA R, LIN Y P. Improved Bayesian spam filtering based on coweighted multiarea information[J]. Lecture Notes in Computer Science, 2005, 3518: 650660.
[45]LI Yang, FANG Binxing, GUO Li, et al. Research of a novel antispam technique based on users’ feedback and improved naive Bayesian approach[C]//Proceedings of the International Conference on Networking and Services. Washington, DC, USA: IEEE Computer Society, 2006: 86. 
[46]SAKKIS G, ANDROUTSOPOULOS I, PALIOURAS G, et al. A memorybased approach to antispam filtering for mailing lists[J]. Information Retrieval, 2003, 6(1): 4973.
[47]SCHAPIRE R E, SINGER Y. BoosTexter: a boostingbased system for text categorization[J]. Machine Learning, 2000, 39(2): 135168.
[48]CARRERAS X, MARQUEZ L. Boosting trees for antispam email filtering[C]//Proceedings of 4th International Conference on Recent Advances in Natural Language Processing. Tzigov Chark, Bulgaria, 2001: 5864.
[49]NICHOLAS T. Using AdaBoost and decision stumps to identify spam email[EB/OL]. [20091107]. http://nlp.stanford.edu/courses/cs224n/2003/fp/tyronen/ report.pdf.
[50]VAPNIK V N. Estimation of dependencies based on empirical data[M]. New York: SpringerVerlag, 1982.[51]VAPNIK V N. The nature of statistical learning theory[M]. 2nd ed. New York: SpringerVerlag, 2000.
[52]DRUCKER H, BURGES C J C, KAUFFMAN L, et al. Support vector regression machines[C]//Advances in Neural Information Processing Systems. Cambridge, USA: MIT Press, 1997: 155161.
[53]DRUCKER H, WU D, VAPNIK V N. Support vector machines for spam categorization[J]. IEEE Transactions on Neural Networks, 1999, 10(5): 10481054.
[54]COHEN W W. Fast effective rule induction[C]//Procee dings of 12th International Conference on Machine Learning. San Mateo, USA: Morgan Kaufmann, 1995: 115123.
[55]SCHAPIRE R E, SINGER Y, SINGHAL A. Boosting and Rocchio applied to text filtering[C]//Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 1998: 215223.
[56]JOACHIMS T. A probabilistic〖KG*1/2〗 analysis〖KG*1/2〗 of〖KG*1/2〗 the 〖KG*1/2〗Rocchio 〖KG*1/2〗algorithm 〖KG*1/2〗with TFIDF for text categorization[C]//Procee dings of 14th International Conference on Machine Learning. San Francisco, USA: Morgan Kaufman Publishers Inc., 1997: 143151.
[57]SASAKI M, SHINNOU H. Spam detection using text clustering[C]//Proceedings of International Conference on Cyberworlds. Washington, DC, USA: IEEE Computer Society, 2005: 316319.
[58]DHILLON I S, MODHA D S. Concept decompositions for large sparse text data using clustering[J]. Machine Learning, 2001, 42(1/2): 143175.
[59]CLARK J, KOPRINSKA I, POON J. A neural network based approach to automated email classification[C]//Proceedings of IEEE/WIC International Conference on Web Intelligence. Washington, DC, USA: IEEE Computer Society, 2003: 702.
[60]STUART I, CHA S H, TAPPERT C. A neural network classifier for junk email[J]. Lecture Notes in Computer Science, 2004, 3163: 442450.
[61]SECKER A, FREITAS A A, TIMMIS J. AISEC: an artificial immune system for email 〖KG*1/3〗classification[C]//Procee dings of the Congress on Evolutionary Computation. Canberra, Australia, 2003: 131139.
[62]ODA T, WHITE T. Spam detection using an artificial immune system[EB/OL]. [20091109]. http://terri.zone12.com/doc/academic/crossroads/.
[63]MEDLOCK B. An adaptive, semistructured language model approach to spam filtering on a new corpus[C]//Proceedings of 3rd Conference on Email and Antispam. Mountain View, USA, 2006.
[64]MEDLOCK B. GenSpam [EB/OL]. [20091109]. http://www.benmedlock.co.uk/genspam.html.
[65]ZHANG L, ZHU J, YAO T. An evaluation of statistical spam filtering techniques[J]. ACM Transactions on Asian Language Information Processing, 2004, 3(4): 243269.
[66]ZHANG L, ZHU J, YAO T. Index of /lzhang10/spam[EB/OL]. [20091109]. http://homepages.inf.ed.ac.uk/lzhang10/spam/.
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备注/Memo

收稿日期:2009-11-20.
基金项目:国家“863”计划资助项目(2007AA01Z453);国家自然科学基金资助项目(60673020,60875080).
通信作者:谭 营.E-mail: ytan@pku.edu.cn.
作者简介:

谭 营,男,1964年生,教授、博士生导师、博士,IEEE Senior Member. IJSIR副编辑,IES Journal B, Intelligent Devices and Systems副编辑,Journal of Computer Science and Systems Biology副编辑, International Journal of KES编委,Springer和多个重要国际期刊的专刊的编辑,ICSI2010大会主席,ISNN2008程序委员会主席.主要研究方向为计算智能、群体智能、智能信息处理、计算机安全、数据挖掘与模式识别等.负责国家“863”计划、国家自然基金等科研项目30余项.获得2009年度国家自然科学奖二等奖.发表学术论文200余篇.
朱元春,男,1985年生,博士研究生,主要研究方向为群体智能、人工免疫系统、智能信息处理算法、计算机安全、模式识别等.

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