Literature Review of SMS Phishing Attacks: Lessons, Addresses, and Future Challenges

Social engineering short message service (SMS) Phishing (smishing) attacks have increased with the rise of smart homes, cities, and devices. Smishing is a form of phishing that involves stealing the victims’ private information through the content they send in their SMS, which has become the most widely used function on mobile devices. This paper aims to explore the different solutions implemented to detect and mitigate this type of attack. To that end, we conduct an exhaustive review using the methodological guide of Barbara Kitchenham. The literature search located 40 articles that met the exclusion and inclusion criteria. The results show a variety of implementations of Random Forest, and Deep Learning techniques (in particular, Long-term memory or LSTM). A few researchers solved the smishing problem using Uniform Resource Locator analysis and blacklists. Others used methods such as Bidirectional Encoder Representations from Transformers embedding, Elliptic Curve Digital Signature Algorithm encryption and convolutional neural networks. In addition, we discovered insights, psychological challenges, and future research directions associated with smishing, such as persuasion and urgency, confirmation bias, and unfamiliarity, indicating that solutions for detecting and mitigating smishing attacks must also consider the study of the human mind and its processes.