Review of Smishing Detection Via Machine Learning

Abstract

Smishing is a cybercriminal attack targeting mobile Short Message Service (SMS) devices that contains a malicious link, phone number, or email. The attacker intends to use this message to steal the victim's sensitive information, such as passwords, bank account details, and credit cards. One method of combating smishing is to raise awareness and educate users about the various tactics used by SMS phishers. But even so, this method has been criticized for becoming inefficient because smishing tactics are continually evolving. A more promising anti-smishing method is to use machine learning. This paper introduces a number of machine learning algorithms that can be used for detecting smishing. Furthermore, the differences and similarities among them as well as the pros and cons of each are presented to support future research into more effective anti-smishing solutions for securing mobile devices from cyber criminals.

SmishGuard: Leveraging Machine Learning and Natural Language Processing for Smishing Detection

“…In this dynamic environment, machine learning (ML) arises as a formidable ally in the defense against smishing attacks [4]. By their very nature, machine learning algorithms excel at pattern recognition, allowing for the detection of nuanced clues within text messages that reveal fraudulent intent.…”