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Spam Detection Using Linear Genetic Programming

  • Clyde Meli
  • Vitezslav Nezval
  • Zuzana Kominkova Oplatkova
  • Victor Buttigieg
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 837)

Abstract

Spam refers to unsolicited bulk email. Many algorithms have been applied to the spam detection problem and many programs have been developed. The problem is an adversarial one and an ongoing fight against spammers. We prove that reliable Spam detection is an NP-complete problem, by mapping email spams to metamorphic viruses and applying Spinellis’s [30] proof of NP-completeness of metamorphic viruses. Using a number of features extracted from the SpamAssassin Data set, a linear genetic programming (LGP) system called Gagenes LGP (or GLGP) has been implemented. The system has been shown to give 99.83% accuracy, higher than Awad et al.’s [3] result with the Naïve Bayes algorithm. GLGP’s recall and precision are higher than Awad et al.’s, and GLGP’s Accuracy is also higher than the reported results by Lai and Tsai [19].

Keywords

Spam detection NP-complete Identification Security Linear genetic programming 

Notes

Acknowledgements

This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme project No. LO1303 (MSMT-7778/2014) and also by the European Regional Development Fund under the project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089 and further it was supported by Grant Agency of the Czech Republic—GACR P103/15/06700S.

This research has in part been carried out using computational facilities procured through the European Regional Development Fund, Project ERDF-076 ‘Refurbishing the Signal Processing Laboratory within the Department of CCE’, University of Malta.

References

  1. 1.
    Almeida, T.A., Yamakami, A.: Advances in spam filtering techniques. In: Computational Intelligence for Privacy and Security, pp. 199–214. Springer, Heidelberg (2012)Google Scholar
  2. 2.
    Androutsopoulos, I., et al.: An experimental comparison of naive bayesian and keyword-based anti-spam filtering with personal e-mail messages. In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 160–167. ACM, New York (2000)Google Scholar
  3. 3.
    Awad, W.A., ELseuofi, S.M.: Machine learning methods for e-mail classification. Int. J. Comput. Appl. 16(1), 0975–8887 (2011)Google Scholar
  4. 4.
    Blickle, T., Thiele, L.: A Comparison of selection schemes used in genetic algorithms. Gloriastrasse 35, CH-8092 Zurich: Swiss Federal Institute of Technology (ETH) Zurich, Computer Engineering and Communications Networks Lab (TIK (1995)Google Scholar
  5. 5.
    Borodin, Y., et al.: Live and learn from mistakes: a lightweight system for document classification. Inf. Process. Manag. 49(1), 83–98 (2013)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Brameier, M.: On linear genetic programming. Fachbereich Informatik, Universität Dortmund (2004)Google Scholar
  7. 7.
    Cid, I., et al.: The impact of noise in spam filtering: a case study. In: Perner, P. (ed.) Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects, pp. 228–241. Springer, Heidelberg (2008)Google Scholar
  8. 8.
    Cormack, G.V., Lynam, T.: TREC 2005 spam track overview. In: The Fourteenth Text REtrieval Conference (TREC 2005) Proceedings (2005)Google Scholar
  9. 9.
    Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman, San Francisco (1979)zbMATHGoogle Scholar
  10. 10.
    Graham, P.: Better Bayesian Filtering. http://www.paulgraham.com/better.html
  11. 11.
    Graham, P.: A Plan for Spam. http://www.paulgraham.com/spam.html
  12. 12.
    Gržinić, T., et al.: CROFlux—Passive DNS method for detecting fast-flux domains. In: 2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1376–1380 (2014)Google Scholar
  13. 13.
    Harris, E.: The Next Step in the Spam Control War: Greylisting. http://projects.puremagic.com/greylisting/whitepaper.html
  14. 14.
    Holz, T., et al.: Measuring and detecting fast-flux service networks. In: 15th Network and Distributed System Security Symposium (NDSS) (2008)Google Scholar
  15. 15.
    Hunt, R., Carpinter, J.: Current and new developments in spam filtering. In: 2006 14th IEEE International Conference on Networks, pp. 1–6 (2006)Google Scholar
  16. 16.
    Gonçalves, I.: Controlling Overfitting in Genetic Programming. CISUG (2011)Google Scholar
  17. 17.
    Juknius, J., Čenys, A.: Intelligent botnet attacks in modern Information warfare. In: 15th International Conference on Information and Software Technology, pp. 37–39 (2009)Google Scholar
  18. 18.
    Kolari, P., et al.: Detecting spam blogs: a machine learning approach. In: Proceedings of the National Conference on Artificial Intelligence, p. 1351. AAAI Press/MIT Press, Menlo Park/Cambridge 1999 (2006)Google Scholar
  19. 19.
    Lai, C.-C., Tsai, M.-C.: An empirical performance comparison of machine learning methods for spam e-mail categorization. In: Fourth International Conference on Hybrid Intelligent Systems, HIS 2004, pp. 44–48 IEEE (2004)Google Scholar
  20. 20.
    Lee, K., et al.: Uncovering social spammers: social honeypots + machine learning. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 435–442 ACM, New York (2010)Google Scholar
  21. 21.
    Sahami, M., et al.: A Bayesian approach to filtering junk e-mail. In: Proceedings of AAAI-98 Workshop on Learning for Text Categorization (1998)Google Scholar
  22. 22.
    Meli, C., Oplatkova, Z.K.: SPAM detection: Naïve Bayesian classification and RPN expression-based LGP approaches compared. In: Software Engineering Perspectives and Application in Intelligent Systems, pp. 399–411. Springer, Heidelberg (2016)Google Scholar
  23. 23.
    Meli, C.: Application and improvement of genetic algorithms and genetic programming towards the fight against spam and other internet malware. Submitted Ph.D. thesis, University of Malta, Malta (2017)Google Scholar
  24. 24.
    Miranda-García, A., Calle-Martín, J.: Yule’s characteristic K revisited. Lang. Resour. Eval. 39(4), 287–294 (2005)CrossRefGoogle Scholar
  25. 25.
    Ntoulas, A., et al.: Detecting spam web pages through content analysis. In: Proceedings of the 15th International Conference on World Wide Web, pp. 83–92. ACM, New York (2006)Google Scholar
  26. 26.
    Oltean, M., Grosan, C.: Evolving evolutionary algorithms using multi expression programming. In: ECAL, pp. 651–658 (2003)Google Scholar
  27. 27.
    Oltean, M., Dumitrescu, D.: Multi expression programming. Babes-Bolyai University (2002)Google Scholar
  28. 28.
    Rao, J.M., Reiley, D.H.: The economics of spam. J. Econ. Perspect. 26(3), 87–110 (2012)CrossRefGoogle Scholar
  29. 29.
    Ruan, G., Tan, Y.: A three-layer back-propagation neural network for spam detection using artificial immune concentration. Soft. Comput. 14(2), 139–150 (2009)CrossRefGoogle Scholar
  30. 30.
    Spinellis, D.: Reliable identification of bounded-length viruses is NP-complete. IEEE Trans. Inf. Theory 49(1), 280–284 (2003)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Stuart, I., et al.: A neural network classifier for junk e-mail. In: Document Analysis Systems VI, pp. 442–450. Springer, Heidelberg (2004)Google Scholar
  32. 32.
    Wang, Z.-Q., et al.: An efficient SVM-based spam filtering algorithm. In: 2006 International Conference on Machine Learning and Cybernetics, pp. 3682–3686. IEEE (2006)Google Scholar
  33. 33.
    Yule, G.U.: On sentence- length as a statistical characteristic of style in prose: with application to two cases of disputed authorship. Biometrika 30(3–4), 363–390 (1939)Google Scholar
  34. 34.
    Zhang, L., et al.: An evaluation of statistical spam filtering. Techniques 3(4), 243–269 (2004)Google Scholar
  35. 35.
    Zhang, M., Fogelberg, C.G.: Genetic programming for image recognition: an LGP approach. In: EvoWorkshops 2007, pp. 340–350. Springer, Heidelberg (2007)Google Scholar
  36. 36.
    RPN, An Introduction To Reverse Polish Notation. http://h41111.www4.hp.com/calculators/uk/en/articles/rpn.html
  37. 37.
    Symantec Internet Security Report (2016). https://resource.elq.symantec.com/LP=2899

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Information SystemsUniversity of MaltaMsidaMalta
  2. 2.Department of Informatics and Artificial IntelligenceTomas Bata UniversityZlínCzech Republic
  3. 3.Department of Communications and Computer EngineeringUniversity of MaltaMsidaMalta

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