An investigation in detection and mitigation of smishing using machine learning techniques

Mobile devices are currently more susceptible to cyberattacks than other types of devices, such as personal computers, as a result of the effects and development and their common usage in daily life. Mobile phones have text messaging capabilities, often known as SMS (Short Text Messages), which are used by cybercriminals to target people. A malicious program that uses text messages to target smartphone users is frequently referred to as SMS phishing. Smishing is a sort of phishing, although there are several ways in which it differs from phishing, including the amount of information that is included in the SMS and the offensive approach. Then 50 literature works are examined that concentrate on smishing detection and prevention related to the mobile application for this survey to highlight the numerous shortcomings of the present methodologies for smishing prevention and identification in mobile apps. The study of numerous approaches will be clarified in this instance based on many aspects, like significant metrics, publication year and journals, numerical values, and other criteria. This paper also discusses possibilities for future research as well as the difficulties encountered with the detection techniques currently employed in mobile applications.