Model For Mitigating Smishing Attacks On Mobile Platforms

Cybercrime has increased as a result of technology improvements and people's increasing reliance on smartphones and other technologies. SMS enables the dissemination of critical information, which is especially significant for non-digital savvy users who are typically the most isolated. Smishing, often known as SMS phishing, is the practice of sending phony text messages to trick someone into divulging personal information or installing malware. In Kenya, smishing crimes have been noted to escalate at a higher rate. However, there is no comprehensive investigation that has been done involving smishing attacks. There are various proposed solutions for mitigating smishing attacks. However, no existing solution authenticates the sender, filters the smishing content from the message, and informs the user of the potentially harmful content. Therefore, this study presents a novel method to authenticate the sender, filter smishing content from the message, and informs the user in case potentially harmful content exists in the message. Python, MYSQL, and Naïve Bayes classifier were used to develop the model. The model will help mobile phone users to identify fraudulent messages sent by smishers quickly and effectively.