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Controlling Spam: Immunity-based Approach

  • Konrad Kawecki
  • Franciszek Seredyński
  • Marek Pilski
Part of the Advances in Soft Computing book series (AINSC, volume 35)

Abstract

Using electronic mail (e-mail) we can communicate freely and almost at no cost. It creates new possibilities for companies that can use e-mail to send advertisements to their clients (that is called direct-mailing). The term spam refers mostly to that kind of advertisements. Massively sent unsolicited e-mails attack many Internet users. Unfortunately, this kind of message can not be filtered out by simple rule-based filters. In this paper we will extend artificial immune system (AIS) proposed in [6] which is based on mammalian immune system and designed to protect users from spam. Generally AIS are also used to detect computer viruses or to detect anomalies in computer networks.

Keywords

Regular Expression Gene Library Spam Detection Lymphocyte Cloning User Interference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    1. S. Augustyniak, Spam costs, anti-spam pays (in Polish), 2004Google Scholar
  2. 2.
    2. L. N. de Castro, F. J. Von Zuben, Learning and Optimization Using the Clonal Selection Principle, IEEE Transactions on Evolutionary Computation, Special Issue on Artificial Immune Systems, 6 (3), pp. 239–251, 2002Google Scholar
  3. 3.
    3. D. Dasgupta, F. Gonzales, An Immunity-based Technique to Characterize Intrusions in Computer Networks, IEEE Trans. on Evolutionary Computation, vol. 6, N3, June 2002, pp. 281–291CrossRefGoogle Scholar
  4. 4.
    4. P. K. Harmer, P. D. Wiliams, G. H. Gunsch, G. B. Lamont, An Artificial Immune System Architecture for Computer Security Applications, IEEE Trans. on Evolutionary Computations Computation, vol. 6, N3, June 2002, pp. 252– 279CrossRefGoogle Scholar
  5. 5.
    5. S. A. Hofmeyr, S. Forrest, Architecture for an Artificial immune system, IEEE Trans. on Evolutionary Computation, vol. 8, N4, June 2000, pp. 443–473Google Scholar
  6. 6.
    6. T. Oda, T. White, Developing an Immunity to Spam, GECCO 2003, pp. 231– 242Google Scholar
  7. 7.
    7. F. Seredyński, P. Bouvry, D. R. Rutkowski, Anomaly Detection System for Network Security: Immunity-based Approach, IIPWM 2005, pp. 486–490Google Scholar
  8. 8.
    8. S. R. White, M. Swimmer, E. J. Pring, W. C. Arnold, D. M. Chess, J. F. Morar, Anatomy of a commercial-grade immune system. Technical report, IBM Thomas J. Watson Research Center, 2002Google Scholar
  9. 9.
    9. S. T. Wierzchoń, Artificial Immune Systems. Theory and Applications (in Polish), 2001Google Scholar
  10. 10.
    10. http://nospam-pl.netGoogle Scholar
  11. 11.
    11. http://spamassassin.apache.org/, spamassassin websiteGoogle Scholar
  12. 12.
    12. http://www.mail-abuse.comGoogle Scholar
  13. 13.
    13. http://www.nwlink.com/~jhanks/spam.html, spam addressesGoogle Scholar
  14. 14.
    14. http://www.sv-cs.com/spam.html, spam wordsGoogle Scholar

Copyright information

© Springer 2006

Authors and Affiliations

  • Konrad Kawecki
    • 2
  • Franciszek Seredyński
    • 1
    • 2
    • 3
  • Marek Pilski
    • 3
  1. 1.Institute of Computer ScienceUniversity of PodlasieSiedlcePoland
  2. 2.Polish-Japanese Institute of Information TechnologyWarsawPoland
  3. 3.Institute of Computer SciencePolish Academy of SciencesWarsawPoland

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