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ChatSpamDetector: Leveraging Large Language Models for Effective Phishing Email Detection
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The proliferation of phishing sites and emails poses significant challenges to existing cybersecurity efforts. Despite advances in malicious email filters and email security protocols, problems with oversight and false positives persist. Users often struggle to understand why emails are flagged as potentially fraudulent, risking the possibility of missing important communications or mistakenly trusting deceptive 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.
Forward citations
Cited by 6 Pith papers
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PiMRef: Detecting and Explaining Ever-evolving Spear Phishing Emails with Knowledge Base Invariants
PiMRef flags spear phishing by verifying that an email's claimed sender identity matches its actual domain in a knowledge base, and that it contains a call to action.
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Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability
Fine-tuned LLMs for phishing detection show a dissociation between self-consistent explanations and classification accuracy, with Llama models scoring high on CC-SHAP but low on phishing detection while Wizard scores ...
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MultiPhishGuard: An Explainable and Adaptive Multi-Agent LLM System for Phishing Email Detection
A five-agent LLM system with learned fusion weights and an adversarial training loop reports 97.89% accuracy and a 95.88% F1 score on pooled public phishing corpora, roughly 20 F1 points above single-agent and chain-o...
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Cyri: A Conversational AI-based Assistant for Supporting the Human User in Detecting and Responding to Phishing Attacks
Cyri detects phishing emails locally with a Llama 3.1 model that extracts semantic persuasion features, explains them in chat, and flags suspicious text in the mail client.
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AdaPhish: AI-Powered Adaptive Defense and Education Resource Against Deceptive Emails
An LLM-based phish bowl that automatically anonymizes reported phishing emails and combines nearest-neighbor retrieval with GPT-4o classification to detect and track new phishing campaigns.
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Enhancing Phishing Email Identification with Large Language Models
Llama-3.1-70b detects phishing emails with 97.21% accuracy and 98.10% precision on a combined, length-filtered dataset of 6,867 emails.
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