Development of a Real Time Smishing Detection Mobile Application using Rule Based Techniques

Abstract

The introduction of alternative messaging platforms on mobile devices have not been able to phase off Short Messaging Service (SMS) as the most widely used means of textual communication. Over the decades, SMS has remained the most responsive way of communication that has been embraced by individuals and organizations in passing information across to their intended recipients. However, hackers have been employing this tool as a way to deceive the gullible into divulging sensitive information about their financial dealings as well as gain access to their mobile devices. A lot of innocent but ignorant individuals have become victims of this smishing acts and have lost huge sum of money as a result. Though existing research have extensively proposed and implemented different techniques for detecting and separating spam SMS from ham SMS, a mobile application that uses a rule-based RIPPER and C4.5 classifiers in detecting smishing acts is proposed. The rule-based classifiers were used to formulate rules used in detecting and separating spam from ham while a mobile application was developed to use the rule-based model in smishing detection. An Application Programming Interface (API) was designed to intercept incoming SMS, forward them to the rule-based model for analysis and then relay the results to the user via the developed mobile application. The user then decides to either retain or discard the SMS.