Our daily lives are significantly impacted by mobile devices. They are employed for a wide range of purposes in the military, government, education, and entertainment sectors. Due to the ease of use, individuals often store sensitive data and perform
critical operations, making them a prime target for attackers. The research addresses the threats of smishing and vishing attacks targeting mobile device users. To address this, machine learning algorithms were designed and developed to identify smishing attacks and a blacklist to identify potential vishing attacks. The models evaluated in this study include the Naïve Bayes, Decision Tree, and Random Forest algorithms. The proposed solution aims to mitigate the risks of smishing and vishing attacks. The results obtained indicated that the Random Forest model performed the best in the selected environment. When addressing vishing attacks, the prototype solely relied on a blacklist which can be valuable, but its effectiveness is reliant on continuous updates and monitoring.

Authors: Aaliyah E Chichwadia, Noluntu Mpekoa

Published in: International Conference on Information Society (i-Society-2024)

• Date of Conference: 26-28 August, 2024
• DOI: 10.20533/iSociety.2024.0018
• ISBN: 978-1-913572-72-3
• Conference Location: Churchill College, Cambridge, UK