Abstract
E-mail is one of the most important applications for all the computer users due to its efficiency and low cost. However, some users use it in sending spam emails, which become a severe problem that has great effect on the users’ performance. E-mail filtering is an important approach to identify those spam emails. In this paper, based on different machine learning algorithms, a novel semantic-based approach for email filtering is proposed. The approach analyses the content of the email and assigns a weight to each term that can help in classifying it into spam or ham email. We enhanced the traditional Email filtering approaches by applying semantic-based feature reduction model using the WordNet ontology in order to handle the high dimensionality problem of feature size. The experiments that have been conducted using Enron dataset showed great results. A comparative study has also been presented among different classifiers that prove the efficiency of the proposed approach. These classifiers are Naïve Bayes (NB), Support Vector Machine (SVM), Logistic Regression, J48 and Random Forest. The Logistic Regression classifier has the best accuracy with value of 0.96. Followed by the NB and SVM that almost have similar results of accuracy value 0.93. Finally, the Random Forest and J48 classifiers have the least accuracy values of 0.85 and 0.87 respectively.
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References
Internet Threats Trend Report. Cyberoam® A SOPHOS Campany (2014)
Castillo, M.D., Serrano, J.I.: An interactive hybrid system for identifying and filtering unsolicited e-mail. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds.) IDEAL 2006. LNCS, vol. 4224, pp. 779–788. Springer, Heidelberg (2006). doi:10.1007/11875581_94
Hristea, F.T.: Semantic WordNet-based feature selection. In: Hristea, F.T. (ed.) The Naïve Bayes Model for Unsupervised Word Sense Disambiguation, pp. 17–33. Springer, Heidelberg (2013)
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, pp. 44–48. IEEE (2004)
Islam, M., Mahmud, A.A., Islam, M.: Machine Learning Approaches for Modeling Spammer Behavior. In: Kan, M.-Y., Lam, W., Nakov, P., Cheng, P.-J. (eds.) AIRS 2010. LNCS, vol. 6458, pp. 251–260. Springer, Heidelberg (2010)
Blanzieri, E., Bryl, A.: A survey of learning-based techniques of email spam filtering. Technical report DIT-06-056, University of Trento, Information Engineering and Computer Science Department (2008)
Mitchell, T.: Generative and discriminative classifiers: naive Bayes and logistic regression (2005). Manuscript http://www.cs.cm.edu/~tom/NewChapters.html
Renuka, D.K., Hamsapriya, T., Chakkaravarthi, M.R., Surya, P.L.: Spam classification based on supervised learning using machine learning techniques. In: International Conference on Process Automation, Control and Computing (PACC), pp. 1–7. IEEE (2011)
Shi, L., Wang, Q., Ma, X., Weng, M., Qiao, H.: Spam email classification using decision tree ensemble. J. Comput. Inf. Syst. 8(3), 949–956 (2012)
Islam, M.R., Zhou, W.: Architecture of adaptive spam filtering based on machine learning algorithms. In: Jin, H., Rana, O.F., Pan, Y., Prasanna, V.K. (eds.) ICA3PP 2007. LNCS, vol. 4494, pp. 458–469. Springer, Heidelberg (2007). doi:10.1007/978-3-540-72905-1_41
Islam, R., Xiang, Y.: Email classification using data reduction method. In: Proceedings of the 5th International ICST Conference on Communications and Networking in China, pp. 1–5. IEEE (2010)
Bhat, V.H., Malkani, V.R., Shenoy, P.D., Venugopal, K.R., Patnaik, L.M.: Classification of email using beaks: behavior and keyword stemming. In: TENCON IEEE Region 10 Conference, pp. 1139–1143. IEEE (2011)
Lee, S.M., Kim, D.S., Kim, J.H., Park, J.S.: Spam detection using feature selection and parameters optimization. In: Intelligent and Software Intensive Systems International Conference on Complex, pp. 883–888. IEEE (2010)
Abdelrahim, A.A., Elhadi, A.A.E., Ibrahim, H., Elmisbah, N.: Feature selection and similarity coefficient based method for email spam filtering. In: International Conference on Computing, Electrical and Electronics Engineering (ICCEEE). IEEE (2013)
Ting, L., Qingsong, Y.: Spam feature selection based on the improved mutual information algorithm. In: Fourth International Conference on Multimedia Information Networking and Security (MINES). IEEE (2012)
Wang, R., Youssef, A.M., Elhakeem, A.K.: On some feature selection strategies for spam filter design. In: Canadian Conference on Electrical and Computer Engineering, pp. 2186–2189, CCECE 2006. IEEE (2006)
Shams, R., Mercer, R.E.: Classifying spam emails using text and readability features. In: 13th International Conference on Data Mining (ICDM). IEEE (2013)
More, S., Kulkarni, S.: Data mining with machine learning applied for email deception. In: International Conference on Optical Imaging Sensor and Security. IEEE (2013)
Sharaff, A., Nagwani, N.K., Dhadse, A.: Comparative study of classification algorithms for spam email detection. In: Emerging Research in Computing, Information, Communication and Applications, pp. 237–244. Springer, India (2016)
Bahgat, E.M., Rady, S., Gad, W.: An e-mail filtering approach using classification techniques. In: Gaber, T., Hassanien, A.E., El-Bendary, N., Dey, N. (eds.) The 1st International Conference on Advanced Intelligent System and Informatics. AISC, vol. 407, pp. 321–331. Springer, Heidelberg (2016). doi:10.1007/978-3-319-26690-9_29
Lu, Z., Ding, J.: An efficient semantic VSM based email categorization method. In: International Conference on Computer Application and System Modeling, vol. 11, pp. 511–525. IEEE (2010)
Yoo, S., Gates, D., Levin, L., Fung, S., Agarwal, S., Freed, M.: Using semantic features to improve task identification in email messages. In: Kapetanios, E., Sugumaran, V., Spiliopoulou, M. (eds.) NLDB 2008. LNCS, vol. 5039, pp. 355–357. Springer, Heidelberg (2008)
Tang, H.J., Yan, D.F., Yuan, T.I.A.N.: Semantic dictionary based method for short text classification. J. China Univ. Posts Telecommun. 20, 15–19 (2013)
Enron-Spam datasets: CSMINING group, http://csmining.org/index.php/enron-spam-datasets.html. Accessed 7 July 2016
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
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Bahgat, E.M., Moawad, I.F. (2017). Semantic-Based Feature Reduction Approach for E-mail Classification. In: Hassanien, A., Shaalan, K., Gaber, T., Azar, A., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016. AISI 2016. Advances in Intelligent Systems and Computing, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-319-48308-5_6
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