Detecting Scams Using Large Language Models

LLMs used to detect scams in cybersecurity, with focus on phishing and fraud. Preliminary evaluation shows effectiveness.

Summary:

• Large Language Models (LLMs) are being explored for scam detection in cybersecurity.
• The paper outlines the steps involved in building an effective scam detector using LLMs.
• Preliminary evaluations using GPT-3.5 and GPT-4 demonstrate their proficiency in recognizing common signs of phishing or scam emails.

Major Findings:

1. LLMs have found various security applications, including phishing detection, sentiment analysis, threat intelligence, malware analysis, and vulnerability assessment.
2. Building an effective scam detector using LLMs involves key steps such as data collection, preprocessing, model selection, training, and integration into target systems.
3. Preliminary evaluations using GPT-3.5 and GPT-4 demonstrate their proficiency in recognizing common signs of phishing or scam emails.

Analysis and Critique:

• The paper focuses on introducing a foundational concept and conducting preliminary evaluations, but a more comprehensive assessment is needed to determine the relative strengths and weaknesses of LLMs across various natural language understanding and generation tasks.
• The effectiveness of LLMs can vary depending on the complexity of the text, training data, fine-tuning methods, and specific versions of the models.
• Collaboration with domain experts and continuous adaptation to emerging threats are vital for ongoing refinement and optimization of LLMs for scam detection.

Appendix

Model	gpt-3.5-turbo-1106
Date Generated	2024-02-26
Abstract	https://arxiv.org/abs/2402.03147v1
HTML	https://browse.arxiv.org/html/2402.03147v1
Truncated	False
Word Count	4001