Abstract
Spam email problem is a major shortcoming of email technology for computer security. In this research, a granular classifier model is proposed to discover hyperboxes in the geometry of information granules for spam detection in three steps. In the first step, the k-means clustering algorithm is applied to find the seed_points to build the granular structure of the spam and non-spam patterns. Moreover, applying the interval analysis through the high homogeneity of the patterns captures the key part of the spam and non-spam classifiers’ structure. In the second step, PSO algorithm is hybridized with the k-means to optimize the formalized information granules’ performance. The proposed model is evaluated based on the accuracy, misclassification and coverage criteria. Experimental results reveal that the performance of our proposed model is increased through applying Particle Swarm Optimization and fuzzy set.
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References
Idris, I., Selamat, A.: Improved email spam detection model with negative selection algorithm and particle swarm optimization. Applied Soft Computing 22, 11–27 (2014)
Salcedo-Campos, F., Díaz-Verdejo, J., García-Teodoro, P.: Segmental parameterisation and statistical modelling of e-mail headers for spam detection. Information Sciences 195, 45–61 (2012)
Méndez, J.R., Reboiro-Jato, M., Díaz, F., Díaz, E., Fdez-Riverola, F.: Grindstone4Spam: An optimization toolkit for boosting e-mail classification. Journal of Systems and Software 85, 2909–2920 (2012)
Bouguila, N., Amayri, O.: A discrete mixture-based kernel for SVMs: application to spam and image categorization. Information Processing & Management 45, 631–642 (2009)
Salehi, S., Selamat, A., Bostanian, M.: Enhanced genetic algorithm for spam detection in email. In: 2011 IEEE 2nd International Conference on Software Engineering and Service Science (ICSESS), pp. 594–597. IEEE (2011)
Guzella, T.S., Mota-Santos, T.A., Uchôa, J.Q., Caminhas, W.M.: Identification of SPAM messages using an approach inspired on the immune system. Biosystems 92, 215–225 (2008)
Salehi, S., Selamat, A.: Hybrid simple artificial immune system (SAIS) and particle swarm optimization (PSO) for spam detection. In: 2011 5th Malaysian Conference in Software Engineering (MySEC), pp. 124–129. IEEE (2011)
Fdez-Riverola, F., Iglesias, E.L., Díaz, F., Méndez, J.R., Corchado, J.M.: SpamHunting: An instance-based reasoning system for spam labelling and filtering. Decision Support Systems 43, 722–736 (2007)
Özgür, L., Güngör, T., Gürgen, F.: Adaptive anti-spam filtering for agglutinative languages: a special case for Turkish. Pattern Recognition Letters 25, 1819–1831 (2004)
Velmurugan, T.: Performance based analysis between k-Means and Fuzzy C-Means clustering algorithms for connection oriented telecommunication data. Applied Soft Computing 19, 134–146 (2014)
Salehi, S., Selamat, A., Fujita, H.: Systematic mapping study on Granular computing. Knowledge-Based Systems (2015)
Pedrycz, W., Park, B.-J., Oh, S.-K.: The design of granular classifiers: A study in the synergy of interval calculus and fuzzy sets in pattern recognition. Pattern Recognition 41, 3720–3735 (2008)
Forrest, S., Perelson, A.S., Allen, L., Cherukuri, R.: Self-nonself discrimination in a computer. In: 2012 IEEE Symposium on Security and Privacy, pp. 202–202. IEEE Computer Society (1994)
Oda, T., White, T.: Developing an immunity to spam. In: Cantú-Paz, E., et al. (eds.) GECCO 2003. LNCS, vol. 2723. Springer, Heidelberg (2003)
Santos, I., Laorden, C., Sanz, B., Bringas, P.G.: Enhanced topic-based vector space model for semantics-aware spam filtering. Expert Systems with applications 39, 437–444 (2012)
Carnap, R.: Meaning and synonymy in natural languages. Philosophical Studies 6, 33–47 (1955)
Polyvyanyy, A.: Evaluation of a novel information retrieval model: eTVSM. Master’s thesis, Hasso Plattner Institut (2007)
Salton, G., McGill, M.J.: Introduction to modern information retrieval (1983)
Laorden, C., Ugarte-Pedrero, X., Santos, I., Sanz, B., Nieves, J., Bringas, P.G.: Study on the effectiveness of anomaly detection for spam filtering. Information Sciences 277, 421–444 (2014)
Zhang, Y., Wang, S., Phillips, P., Ji, G.: Binary PSO with mutation operator for feature selection using decision tree applied to spam detection. Knowledge-Based Systems 64, 22–31 (2014)
DeBarr, D., Wechsler, H.: Spam detection using random boost. Pattern Recognition Letters 33, 1237–1244 (2012)
Asuncion, A., Newman, D.: UCI machine learning repository (2007)
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Salehi, S., Selamat, A., Krejcar, O., Kuca, K. (2015). Fuzzy Granular Classifier Approach for Spam Detection. In: Núñez, M., Nguyen, N., Camacho, D., Trawiński, B. (eds) Computational Collective Intelligence. Lecture Notes in Computer Science(), vol 9330. Springer, Cham. https://doi.org/10.1007/978-3-319-24306-1_25
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DOI: https://doi.org/10.1007/978-3-319-24306-1_25
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