A New Binary Particle Swarm Optimization for Feature Subset Selection with Support Vector Machine

Abstract

Social Engineering (SE) has emerged as one of the most familiar problem concerning organizational security and computer users. At present, the performance deterioration of phishing and spam detection systems are attributed to high feature dimensionality as well as the computational cost during feature selection. This consequently reduces the classification accuracy or detection rate and increases the False Positive Rate (FPR). This research is set to introduce a novel feature selection method called the New Binary Particle Swarm Optimization (NBPSO) to choose a set of optimal features in spam and phishing emails. The proposed feature selection method was tested in a classification experiments using the Support Vector Machine (SVM) to classify emails according to the various features as input. The results obtained by experimenting on two phishing and spam emails showed a reasonable performance to the phishing detection system.