  The proliferation of phishing sites and emails poses significant challenges
to existing cybersecurity efforts. Despite advances in spam filters and email
security protocols, problems with oversight and false positives persist. Users
often struggle to understand why emails are flagged as spam, risking the
possibility of missing important communications or mistakenly trusting phishing
emails.
  This study introduces ChatSpamDetector, a system that uses large language
models (LLMs) to detect phishing emails. By converting email data into a prompt
suitable for LLM analysis, the system provides a highly accurate determination
of whether an email is phishing or not. Importantly, it offers detailed
reasoning for its phishing determinations, assisting users in making informed
decisions about how to handle suspicious emails. We conducted an evaluation
using a comprehensive phishing email dataset and compared our system to several
LLMs and baseline systems. We confirmed that our system using GPT-4 has
superior detection capabilities with an accuracy of 99.70%. Advanced contextual
interpretation by LLMs enables the identification of various phishing tactics
and impersonations, making them a potentially powerful tool in the fight
against email-based phishing threats.
