K or S mishing Explainer: A K orean-centric LLM -based Framework for Smishing Detection and Explanation Generation (2024)

Abstract: To mitigate the annual financial losses caused by SMS phishing (smishing) in South Korea, we propose an explainable smishing detection framework that adapts to a Korean-centric large language model (LLM). Our framework not only classifies smishing attempts but also provides clear explanations, enabling users to identify and understand these threats. This end-to-end solution encompasses data collection, pseudo-label generation, and parameter-efficient task adaptation for models with fewer than five billion parameters. Our approach achieves a 15% improvement in accuracy over GPT-4 and generates high-quality explanatory text, as validated by seven automatic metrics and qualitative evaluation, including human assessments.

## introduction smishing, a form of financial fraud through sms, has evolved into deceptive messages aimed at stealing personal information or coercing monetary transfers and has led to significant financial losses in south korea @xcite . these losses amounted to approximately usd 100 million in 2021, affecting both individuals and financial institutions @xcite . previous research on smishing detection has focused on a binary classification model that often achieves high accuracy @xcite @xcite . however, the lack of interpretability undermines user trust and practical applicability @xcite @xcite . we introduce a novel framework for adapting a large language model (llm) for explainable smishing detection in korean. this framework enables the model to detect smishing and explain the results of its detection. designed for integration into * these authors contributed equally to this work. enterprise-level services, it aims to maintain robustness to minor text format variations and cost efficiency, surpassing openai's gpt models with prompt engineering in performance. our framework includes pseudo-label generation with a collected dataset and task-adaptive fine-tuning layers to optimize llms for both detection and explanation generation tasks, enhancing users' understanding of the model's results and supporting informed decision-making. additionally, our framework ensures efficiency with models under five billion parameters, outperforming the accuracy and practicality of openai's gpt models. the framework guarantees higher accuracy, practical utility, and cost efficiency, as illustrated in figure 1 . to the best of our knowledge, this study is the first to propose an explainable nlp framework within the smishing detection. the model trained through this framework has significantly positive societal impacts in south korea, highlighting its po-tential to reduce financial fraud through improved detection and enhanced user comprehension. ## methodology our framework is designed to adapt a koreancentric llm for smishing detection and to generate detailed explanations for the results. as illustrated in figure 2 , it consists of two core components: pseudo-label generation and task-adaptive fine-tuning. these elements work together to enhance the model's accuracy in classifying and explaining smishing attempts. ## conclusion this study introduces a framework for explainable smishing detection using a korean llm, designed for enterprise-level applications. by incorporating pseudo-label generation and task-adaptive finetuning, our framework improves the accuracy of smishing detection and generates clear, logical explanations comparable to those from gpt-4o. for service applicability, it ensures both efficiency and accuracy in training and inference with a model under five billion parameters. furthermore, this research demonstrates the effectiveness of adapting non-english llms for explainable smishing detection. by providing more comprehensible explanations, it helps users more effectively identify smishing messages. the proposed framework contributes to the prevention of financial fraud and has a positive social impact.