Data Augmentation for Smishing Detection: A Theory-based Prompt Engineering Approach
Ho Sung Shim, H. Park, Kyuhan Lee, Jang-Sun Park, Seonhye Kang · 2024
Smishing, which refers to social engineering attacks delivered through mobile devices such as smartphones, poses significant threats, yet limited data hinder the development of effective countermeasures. To tackle this, we propose a novel prompt engineering method for data augmentation in smishing detection. Distinguished by its utilization of insights from social science on smishing mechanisms, our approach offers a promising avenue for improving machine learning models in combating smishing attacks.