Fuzzy granular classifier approach for spam detection

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.