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Detection Efficiency Improvement in Multi–component Anti-spam Systems

  • Tomas SochorEmail author
Conference paper
  • 64 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1231)

Abstract

Multi–layer spam detection systems frequently used in many SMTP servers often suffer from a lack of mutual communication between individual layers. The paper presents the construction of a feedback interconnection between two significant layers, namely Message contents check and Greylisting. The verification in a real SMTP server is performed, demonstrating considerable improvement of spam detection efficiency comparing the previous period with missing interconnection, while for a short testing period. Despite the limited generalizability of the result, it suggests the easy way how spam detection can be improved.

Keywords

Spam detection Multi–layer detection Blacklisting Greylisting Message contents check SMTP dialog 

References

  1. 1.
    Email & Spam data. Talos Intelligence. https://www.talosintelligence.com
  2. 2.
    Mehta, B., Hofmann, T.A.: Survey of attack-resistant collaborative filtering algorithms. IEEE Data Eng. Bull. 31(2), 14–22 (2008)Google Scholar
  3. 3.
    Levine J.: DNS blacklists and whitelists. IETF RFC 5782 (2010). https://tools.ietf.org/html/rfc5782
  4. 4.
    Harris, E.: The next step in the spam control war: greylisting (2003). http://www.projects.puremagic.com/greylisting/whitepaper.html
  5. 5.
    Krause, T., Uetz, R., Kretschmann, T.: Recognizing email spam from meta data only. In: IEEE Conference on Communications and Network Security, pp. 178–186. IEEE (2019)Google Scholar
  6. 6.
    Habib, M., Faris, H., Hassonah, M.A., Alqatawna, J., Sheta, A.F., Al-Zoubi, A.M.: Automatic email spam detection using genetic programming with SMOTE. In: ITT 2018 - Information Technology Trends: Emerging Technologies for Artificial Intelligence, pp. 185–190 (2019)Google Scholar
  7. 7.
    Katasev, A.S., Emaletdinova, L.Y., Kataseva, D.V.: Neural network spam filtering technology. In: 2018 International Conference on Industrial Engineering, Applications and Manufacturing (2018)Google Scholar
  8. 8.
    Sochor, T.: Overview of e-mail SPAM elimination and its efficiency. In: IEEE 8th International Conference on Research Challenges in Information Science, pp. 191–201. IEEE (2014)Google Scholar
  9. 9.
    Lysenko, S., Savenko, O., Bobrovnikova, K., Kryshchuk, A.: Self-adaptive system for the corporate area network resilience in the presence of botnet cyberattacks. In: Gaj, P., Sawicki, M., Suchacka, G., Kwiecień, A. (eds.) Computer Networks 2018. CCIS, vol. 860, pp. 385–401. Springer, Heidelberg (2018).  https://doi.org/10.1007/978-3-319-92459-5_31CrossRefGoogle Scholar
  10. 10.
    Bartos, J., Walek, B., Klimes C., Farana R. Fuzzy application with expert system for conducting information security risk analysis. In: European Conference on Information Warfare and Security, ECCWS, pp. 33–41. University of Piraeus. Piraeus (2014)Google Scholar
  11. 11.
    Sochor, T., Davidova, A.: Potential of multilevel SPAM protection in the light of current SPAM trends. In: 10th IEEE International Conference on Networking, Sensing and Control. IEEE (2013)Google Scholar
  12. 12.
    Sochor, T., Farana, R.: Improving efficiency of e-mail communication via SPAM elimination using blacklisting. In: 21st Telecommunications Forum TELFOR School of Electrical Engineering, University of Belgrade (2013)Google Scholar
  13. 13.
    Tayal, D.K., Jain, A., Meena, K.: Development of anti-spam technique using modified K-Means & Naive Bayes algorithm. In: Proceeding of the 3rd International Conference on Computing for Sustainable Global Development, INDIACom (2016)Google Scholar
  14. 14.
    JASP Team. JASP (v 0.11.1) [Computer software] (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Science, Department of Informatics and ComputersUniversity of OstravaOstravaCzechia

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