Scholarlink

With the rise of sophisticated scam websites that exploit human psychological vulnerabilities, distinguishing between legitimate and scam websites has become increasingly challenging. This paper presents ScamFerret, an innovative agent system employing a large language model (LLM) to autonomously collect and analyze data from a given URL to determine whether it is a scam. Unlike traditional machine learning models that require large datasets and feature engineering, ScamFerret leverages LLMs’ natural language understanding to accurately identify scam websites of various types and languages without requiring additional training or fine-tuning. Our evaluation demonstrated that ScamFerret achieves 0.972 accuracy in classifying four scam types in English and 0.993 accuracy in classifying online shopping websites across three different languages, particularly when using GPT-4. Furthermore, we confirmed that ScamFerret collects and analyzes external information such as web content, DNS records, and user reviews as necessary, providing a basis for identifying scam websites from multiple perspectives. These results suggest that LLMs have significant potential in enhancing cybersecurity measures against sophisticated scam websites.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

ScamFerret: Detecting Scam Websites Autonomously with Large Language Models

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Hiroki Nakano,
  • Takashi Koide,
  • Daiki Chiba

摘要

With the rise of sophisticated scam websites that exploit human psychological vulnerabilities, distinguishing between legitimate and scam websites has become increasingly challenging. This paper presents ScamFerret, an innovative agent system employing a large language model (LLM) to autonomously collect and analyze data from a given URL to determine whether it is a scam. Unlike traditional machine learning models that require large datasets and feature engineering, ScamFerret leverages LLMs’ natural language understanding to accurately identify scam websites of various types and languages without requiring additional training or fine-tuning. Our evaluation demonstrated that ScamFerret achieves 0.972 accuracy in classifying four scam types in English and 0.993 accuracy in classifying online shopping websites across three different languages, particularly when using GPT-4. Furthermore, we confirmed that ScamFerret collects and analyzes external information such as web content, DNS records, and user reviews as necessary, providing a basis for identifying scam websites from multiple perspectives. These results suggest that LLMs have significant potential in enhancing cybersecurity measures against sophisticated scam websites.