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International Conference Human Society@Internet

HSI 2003: Web and Communication Technologies and Internet-Related Social Issues — HSI 2003 pp 218–227Cite as

Collaborative Junk E-mail Filtering Based on Multi-agent Systems

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2713))

Abstract

Recently junk e-mail has been one of the most serious information overloading problems. This paper proposes multi-agent system to collaboratively filter spams from users’ mail stream. This multi-agent system is organized by personal agents automatically extracting features based on users’ manual filtering and facilitator managing knowledge extracted by personal agents. Especially, personal agents can analyze junk e-mails for extracting keyphrases and communicate with the others. Due to the domain specific properties of junk e-mail filtering we have formalized the features extracted from e-mail to be highly understandable and efficiently sharable. Thereby, we have defined two types of features in e-mail as apriori feature and keyphrase-based conceptual one. Besides, these features are integrated in the blackboard system of facilitator for collaborative learning. Finally, we show the filtering performance of collaborative learning by comparing with that of personal agent.

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References

  1. Internet E-mail Corporate Usage Report. http://www.securitymanagement.com/library/worldtalk0200.html (2000)

  2. Cohen, W.W.: Learning rules that classify e-mail. Proc. 1996 AAAI Spring Symposium on Machine Learning in Information Access (1996)

    Google Scholar 

  3. Sahami, M., Dumais, S., Heckerman, D., and Horvitz, E.: A bayesian approach to filtering junk e-mail. AAAI Workshop on Learning for Text Classification (1998)

    Google Scholar 

  4. Schapire, R. E., Singer, Y.: BoosTexter: A Boosting-based System for Text Categorization. Machine Learning 39 (2000) 135–168

    Article  MATH  Google Scholar 

  5. Drucker, H., Wu, D., Vapnik, V. N.: Support Vector Machines for Spam Categorization. IEEE Transaction on Neural Networks, Vol. 10, No. 5 (1999)

    Google Scholar 

  6. Joachims, T.: A probabilistic analysis of the Ricchio algorithm with TFIDF for text categorization. Proceedings of 14th International Conference on Machine Learning (1997)

    Google Scholar 

  7. Quinlan, J.R.: C4.5: Programs for Machine Learning. San Mateo, Calif., Morgan Kaufmann Publishers (1993)

    Google Scholar 

  8. Maes, P.: Agents that Reduce Work and Information Overload. Communications of the ACM. 37(7) (1994) 31–40

    Article  Google Scholar 

  9. Ollerenshaw, Z.: Spam, Spam, Spam, Spam.... Computer Fraud & Security, 2000(1) (2000) 13–14

    Article  Google Scholar 

  10. Yu, B., Singh, M.P.: A social mechanism of reputation management in electronic communities. Proceedings of Fourth International Workshop on Cooperative Information Agents (2000) 154–165

    Google Scholar 

  11. Crocker, D.H.: Standard For The Format Of ARPA Internet Text Messages. ftp://ftp.rfc-editor.org/in-notes/rfc822.txt (1982)

  12. Quinlan, J. R.: Induction of decision trees. Machine Learning 1(1) (1986) 81–106

    Google Scholar 

  13. Boone, G.: Concept Features in Re:Agent, an Intelligent Email Agent. Proceedings of the Second International Conference on Autonomous Agents (1998)

    Google Scholar 

  14. Turney, P.D.: Learning Algorithms for Keyphrase Extraction. Information Retrieval 2(4) (2000) 303–336

    Article  Google Scholar 

  15. Ferber, J.: Multi-Agent Systems-An Introduction to Distributed Artificial Intelligence. Addison-Wesley (1999)

    Google Scholar 

  16. Gruber, T.R.: A translation approach to portable ontologies. Knowledge Acquisition, 5(2) (1993) 199–220

    Article  Google Scholar 

  17. Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. ACM Press, N.Y., Addison-Wesley (1999) 29–30

    Google Scholar 

  18. JavaMailℳ API. http://java.sun.com/products/javamail/index.html

  19. AgentBuilder. http://www.agentbuilder.com

  20. Weiss, G. (ed.): Multiagent Systems-A Modern Approach to Distributed Artificial Intelligence. The MIT Press, Cambridge, Massachusetts London, England (1999)

    Google Scholar 

  21. Giraud-Carrier, C.: A Note on the Utility of Incremental Learning. AI Communications, 13(4) (2000) 215–223

    MATH  Google Scholar 

  22. Klusch, M. (ed.): Intelligent Information Agents-Agent-Based Information Discovery and Management on the Internet. Springer, Berlin (1999)

    Google Scholar 

  23. Jung, J. J., Yoon, J.-S., Jo, G.-S.: Collaborative Information Filtering by Using Categorized Bookmarks on the Web. In: Bartenstein, O., Geske, U., Hannebauer, M., Yoshie, O. (eds.): Web-Knowledge Management and Decision Support. Lecture Notes in Artificial Intelligence, Vol. 2543. Springer-Verlag, Berlin Heidelberg New York (2002) 243–257

    Google Scholar 

  24. UMBC KQML Web. http://www.cs.umbc.edu/kqml/

  25. JKQML. http://www.alphaworks.ibm.com/tech/jkqml

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Author information

Authors and Affiliations

  1. Intelligent E-Commerce Systems Laboratory School of Computer Engineering, Inha University, 253 Yonghyun-dong, Incheon, Korea, 402-751

    Jason J. Jung

  2. School of Computer Engineering, Inha University, 253 Yonghyun-dong, Incheon, Korea, 402-751

    Geun-Sik Jo

Authors
  1. Jason J. Jung
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  2. Geun-Sik Jo
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Editor information

Editors and Affiliations

  1. Division of Computer Science, Korea Advanced Institute of Science and Technology, 373-1 Guseong-Dong, Yuseong-Gu, Daejeon, 305-701, Korea

    Chin-Wan Chung

  2. Dept. of Computer Engineering, Seoul National University, San 56-1, Shillim-Dong, Gwanak-Gu, Seoul, 151-742, Korea

    Chong-Kwon Kim

  3. Cyber Database Solutions, Inc., 3445 Executive Center Drive, Suite 256, TX, 78731, USA

    Won Kim

  4. Dept. of Computer Science, National University of Singapore, 3 Science Drive 2, Singapore, 117543

    Tok-Wang Ling

  5. Korea Network Information Center, 11F, KTF B/D 1321-11, Seocho2-Dong, Seocho-Gu, Seoul, 137-857, Korea

    Kwan-Ho Song

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© 2003 Springer-Verlag Berlin Heidelberg

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Jung, J.J., Jo, GS. (2003). Collaborative Junk E-mail Filtering Based on Multi-agent Systems. In: Chung, CW., Kim, CK., Kim, W., Ling, TW., Song, KH. (eds) Web and Communication Technologies and Internet-Related Social Issues — HSI 2003. HSI 2003. Lecture Notes in Computer Science, vol 2713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45036-X_22

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  • DOI: https://doi.org/10.1007/3-540-45036-X_22

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40456-9

  • Online ISBN: 978-3-540-45036-8

  • eBook Packages: Springer Book Archive

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