SMS Phishing Detection Using Oversampling and Feature Optimization Method

Abstract

In this paper, a new SMS phishing detection method using oversampling and feature optimization technology is proposed to improve SMS phishing detection accuracy. Three types features are presented including token features, topic features and Linguistic Inquiry and Word Count (LIWC) features. One of the existing oversampling methods called Adaptive Synthetic Sampling Approach is applied in this paper since it has good performance. Then, Binary Particle Swarm Optimization(BPSO) algorithm is used to analyze the three types features and select the optimal combination of all the features. Finally, the detection results are achieved by Random Forest classification algorithm. Experimental results show oversampling method and feature optimization method improve the accuracy of SMS phishing detection. The best accuracy of the proposed method is 99.01% with an average of 86.6 features. The results demonstrate that the proposed method has a promising performance for SMS phishing detection.