Affiliations: [a] UTM-IRDA Digital Media Centre of Excellence, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia
| [b] Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia
| [c]
University of Hradec Kralove, FIM, Center for Basic and Applied Research, Rokitanskeho, Hradec Kralove, Czech Republic
| [d] Faculty of Technology and Applied Sciences, Al Quds Open University (QOU), AL-Bireh, Rammallah, Palestine
Correspondence:
[*]
Corresponding author. Ali Selamat, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia. Tel.: +6 07 5533034; Fax: +6 07 5566164; E-mail: aselamat@utm.my.
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 hyper-boxes 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, the key part of the spam and non-spam classifiers’ structure is captured by applying the interval analysis through the high homogeneity of the patterns. In the second step, PSO algorithm is hybridized with the k-means to optimize the formalized information granules’ performance. The size of the hyperboxes is expanded away from the center of the granules by PSO. There are some patterns that do not placed in any of the created clusters and known as noise points. In the third step, the membership function in fuzzy sets is applied to solve the noise points’ problem by allocating the noise points through the membership grades. 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.
