Using Machine Learning for Efficient Smishing Detection
Sheng-Shan Chen, Chin‐Yu Sun, Tun‐Wen Pai · 2023
The prevalence of mobile phones and messaging apps has made smishing attacks a growing concern. Attackers use smishing to deceive individuals into revealing sensitive information, downloading malicious software, or performing other harmful actions. These disregarded responses cause significant financial loss to individuals or organizations. We proposed using a machine learning model to analyze message contents and detect phishing URLs. Our experiments collected and manually annotated text messages from mobile phones, and both natural language processing techniques and machine-learning algorithms were applied to the dataset simultaneously. The results show that our proposed method achieved an F1 score of 94%, showing high effectiveness and efficiency in accurately detecting Smishing attacks.