Enhancing Phishing Email Identification with Large Language Models

TL;DR

This paper investigates the use of large language models for detecting phishing emails, demonstrating high accuracy and interpretability, which could improve cybersecurity defenses against sophisticated attacks.

Contribution

The study evaluates the effectiveness of large language models in phishing detection, highlighting their high accuracy and interpretability compared to traditional methods.

Findings

LLMs achieve high accuracy in phishing email detection

LLMs provide interpretable evidence for their decisions

The approach enhances cybersecurity measures against phishing

Abstract

Phishing has long been a common tactic used by cybercriminals and continues to pose a significant threat in today's digital world. When phishing attacks become more advanced and sophisticated, there is an increasing need for effective methods to detect and prevent them. To address the challenging problem of detecting phishing emails, researchers have developed numerous solutions, in particular those based on machine learning (ML) algorithms. In this work, we take steps to study the efficacy of large language models (LLMs) in detecting phishing emails. The experiments show that the LLM achieves a high accuracy rate at high precision; importantly, it also provides interpretable evidence for the decisions.