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A modified content-based evolutionary approach to identify unsolicited emails

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Abstract

This computational research seeks to classify unsolicited versus legitimate emails. A modified version of an existing genetic programming (GP) classifier—i.e., modified genetic programming (MGP)—is implemented to build an ensemble of classifiers to identify unsolicited emails. The proposed classifier is assessed using informative features extracted from two corpora (Enron and SpamAssassin) with the help of the greedy stepwise feature search method. Further, a comparative study is performed with other popular classifiers, such as Bayesian network, naïve Bayes, decision tree, random forest (RF), support vector machine (SVM), and GP. Further the results are validated with 20-fold cross-validation and paired T test. The results prove that the proposed classifier performs better in terms of accuracy and false-positive detection in comparison with the other machine learning classifiers tested in this study. Using different training and testing a set of email files from the Enron corpus, ensemble-based classifiers, such as boosted SVM, boosted Bayesian, boosted naïve Bayesian, RF, and the proposed MGP classifier, are tested and compared on all metrics, including training and testing time. The findings suggest that the MGP classifier with the greedy stepwise feature search method offers an improvement over alternative methods in detecting unsolicited emails.

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References

  1. Cisco Systems, SenderBase real-time threat monitoring system, http://www.senderbase.org

  2. Lai CC (2007) An empirical study of three machine learning methods for spam filtering. Knowl Based Syst 20(3):249–254

    Article  Google Scholar 

  3. Trivedi SK, Dey S (2013) Interplay between probabilistic classifiers and boosting algorithms for detecting complex unsolicited emails. J Adv Comput Netw 1(2):132–136

    Article  Google Scholar 

  4. Clark KP (2008) A survey of content-based spam classifiers

  5. Jakobsson M (2016) Traditional countermeasures to unwanted email. In: Understanding social engineering based scams, pp 51–62. Springer, New York

  6. Cole WK (2007) Blacklists, Blocklists, DNSBL’s, and survival. Retrieved on, 01-26

  7. Leiba B, Ossher J, Rajan VT, Segal R, Wegman MN (2005). SMTP Path Analysis. In: CEAS

  8. Levine JR (2005) Experiences with Greylisting. In: CEAS

  9. Sharaff A, Nagwani NK, Dhadse A (2016). Comparative study of classification algorithms for spam email detection. In: Emerging research in computing, information, communication and applications, pp 237–244. Springer India

  10. Androutsopoulos I, Koutsias J, Chandrinos K V, Paliouras G, Spyropoulos CD (2000) An evaluation of naive bayesian anti-spam filtering, pp 26–28

  11. Sharma AK, Sahni S (2011) A comparative study of classification algorithms for spam email data analysis. Int J Comput Sci Eng 3(5):1890–1895

    Google Scholar 

  12. Drucker H, Wu D, Vapnik VN (1999) Support vector machines for spam categorization. IEEE Trans Neural Netw 10(5):1048–1054

    Article  Google Scholar 

  13. Trivedi SK, Dey S (2014) A study of Ensemble based evolutionary classifiers for detecting unsolicited emails. In: Proceedings of the 2014 conference on research in adaptive and convergent systems (pp 46–51). ACM

  14. Li J, Ping W (2009) The e-mail filtering system based on improved genetic algorithm. In: Proceedings of the 2009 international workshop on information security and application (IWISA 2009), ISBN 978-952-5726-06-0. 16

  15. Espejo PG, Ventura S, Herrera F (2010) A survey on the application of genetic programming to classification. IEEE Trans Syst Man Cybern Part C Appl Rev 40(2):121–144

    Article  Google Scholar 

  16. Sahami M, Dumais S, Heckerman D, Horvitz E (1998) A Bayesian approach to filtering junk e-mail. In: Learning for Text Categorization: Papers from the 1998 workshop, Vol 62, pp 98–105

  17. Rennie J (2000) ifile: An application of machine learning to e-mail filtering. In: Proc. KDD 2000 workshop on text mining, Boston, MA

  18. Pantel P, Lin D (1998) Spamcop: a spam classification and organization program. In: Proceedings of AAAI-98 workshop on learning for text categorization, pp 95–98

  19. Trivedi SK, Dey S, Shikhar P (2013) Effect of various kernels and feature selection methods on SVM performance for detecting email spams. Int J Comput Appl 66(21)

  20. Sakkis G, Androutsopoulos I, Paliouras G, Karkaletsis V, Spyropoulos CD, Stamatopoulos P (2001) Stacking classifiers for anti-spam filtering of e-mail. arXiv preprint cs/0106040

  21. Carreras X, Màrquez L (2001) Boosting trees for clause splitting. In: Proceedings of the 2001 workshop on computational natural language learning-Volume 7 (p 26). Association for Computational Linguistics

  22. Trivedi SK, Dey S (2013) Interplay between probabilistic classifiers and boosting algorithms for detecting complex unsolicited emails. J Adv Comput Netw 1(2):132–136

    Article  Google Scholar 

  23. Rios G, Zha H (2004) Exploring Support vector machines and random forests for spam detection. In: CEAS

  24. Trivedi S, Dey S (2013) Effect of feature selection methods on machine learning classifiers for detecting email spams. In: Proceedings of the 2013 ACM research in applied computation symposium, Montreal, QC, canada

  25. Colleoni E, Rozza A, Arvidsson A (2014) Echo chamber or public sphere? Predicting political orientation and measuring political homophily in Twitter using big data. J Commun 64(2):317–332

    Article  Google Scholar 

  26. Trivedi SK, Dey S (2016) A comparative study of various supervised feature selection methods for spam classification. In: Proceedings of the second international conference on information and communication technology for competitive strategies (p 64). ACM

  27. John GH, Kohavi R, Pfleger K (1994) Irrelevant features and the subset selection problem. In: Machine Learning: Proceedings of the eleventh international conference, pp 121–129

  28. Aas K, Eikvil L (1999) Text categorisation: a survey. Raport NR, 941

  29. Shengen L, Xiaofei N, Peiqi L, Lin W (2011) Generating new features using genetic programming to detect link spam. In: 2011 International conference on intelligent computation technology and automation (ICICTA), vol 1, pp 135–138. IEEE

  30. Trivedi SK, Dey S (2013) An enhanced genetic programming approach for detecting unsolicited emails. In: 2013 IEEE 16th international conference on computational science and engineering (CSE), pp 1153–1160. IEEE

  31. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge

    MATH  Google Scholar 

  32. De Jong K (1975) The analysis of the behavior of class of genetic adaptive systems, doctorate these. Department of computer Science, University of Michigan, Ann Arbor

    Google Scholar 

  33. Grefenstette JJ (1986) Optimization of control parameters for genetic algorithms. IEEE Trans Sys Man Cybern 16(1):122–128

    Article  Google Scholar 

  34. Kapoor V, Dey S, Khurana AP (2011) An empirical study of the role of control parameters of genetic algorithms in function optimization problems. Int J Comput Appl 31(6):20–26

    Google Scholar 

  35. Lewis DD Naive (Bayes) at forty: The independence assumption in information retrieval. In: Proceedings of 10th European conference on machine learning (ECML-98), 1998, pp 4–15

  36. Trivedi SK, Dey S (2016) A combining classifiers approach for detecting email spams. In: 2016 30th international conference on advanced information networking and applications workshops (WAINA), pp 355–360. IEEE

  37. Tripathi A, Trivedi SK (2016) Sentiment analysis of Indian movie review with various feature selection techniques. In IEEE international conference on advances in computer applications (ICACA), pp 181–185. IEEE

  38. Trivedi SK (2016) A study of machine learning classifiers for spam detection. In 2016 4th international symposium on computational and business intelligence (ISCBI), pp 176–180. IEEE

  39. Vapnik VN (1999) An overview of statistical learning theory. In: IEEE transactions on neural network, Vol 10, No 5, pp 988–998. 6

  40. Trivedi SK, Dey S (2014) Interaction between feature subset selection techniques and machine learning classifiers for detecting unsolicited emails. ACM SIGAPP Appl Comput Rev 14(1):53–61

    Article  Google Scholar 

  41. Obimbo C, Nyakundi E Comparison of SVMs with radial-basis function & polynomial kernels in classification of categories in Intrusion Detection

  42. Trivedi SK, Trivedi SK, Dey S, Dey S (2016) A novel committee selection mechanism for combining classifiers to detect unsolicited emails. VINE J Inf Knowl Manag Syst 46(4):524–548

    Article  Google Scholar 

  43. Sharma A, Dey S (2013) A boosted SVM based sentiment analysis approach for online opinionated text. In: Proceedings of the 2013 research in adaptive and convergent systems, pp 28–34. ACM

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Authors and Affiliations

  1. Indian Institute of Management Sirmaur, Sirmaur, HP, India

    Shrawan Kumar Trivedi

  2. Indian Institute of Management Indore, Indore, MP, India

    Shubhamoy Dey

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  1. Shrawan Kumar Trivedi
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  2. Shubhamoy Dey
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Correspondence to Shrawan Kumar Trivedi.

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Trivedi, S.K., Dey, S. A modified content-based evolutionary approach to identify unsolicited emails. Knowl Inf Syst 60, 1427–1451 (2019). https://doi.org/10.1007/s10115-018-1271-1

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  • DOI: https://doi.org/10.1007/s10115-018-1271-1

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