A Practical Rule Based Technique by Splitting SMS Phishing from SMS Spam for Better Accuracy in Mobile Device

Abstract

Short Message Service (SMS) is one of the popular communication services. However, this can contribute to increasing mobile device attacks. Presently, SMS phishing (SMiShing) attack is alarming to the mobile phone users because these attacks usually succeed in stealing information and money. Moreover, SMS phishing and spam are two different types of attack and level of risk. Thus, it is important to have a SMS phishing corpus. The established SMS corpus is limited to spam and none can be found suitable for SMS Phishing. This study proposes a technique to split the class of SMS phishing from SMS spam and produce better accuracy using the Bayesian technique. The result shows that the enhanced SMS corpus gets 99.8064% accurate classification. The study identified classes and generated an improvement of SMS Phishing corpus which has been labelled in three different classes ie., Ham, Spam and Phishing with better accuracy.