@inproceedings{630518d3f20840d1b3f7006780a15e79,
title = "Data Augmentation for Smishing Detection: A Theory-based Prompt Engineering Approach",
abstract = "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.",
keywords = "Artificial Intelligence, Data Augmentation, Machine Learning, Natural Language Processing, Prompt Engineering, Smishing",
author = "Shim, \{Ho Sung\} and Hyoungjun Park and Kyuhan Lee and Park, \{Jang Sun\} and Seonhye Kang",
note = "Publisher Copyright: {\textcopyright} 2024 Copyright is held by the owner/author(s).; 33rd Companion of the ACM World Wide Web Conference, WWW 2023 ; Conference date: 13-05-2024 Through 17-05-2024",
year = "2024",
month = may,
day = "13",
doi = "10.1145/3589335.3651903",
language = "English (US)",
series = "WWW 2024 Companion - Companion Proceedings of the ACM Web Conference",
publisher = "Association for Computing Machinery, Inc",
pages = "1327--1328",
booktitle = "WWW 2024 Companion - Companion Proceedings of the ACM Web Conference",
}