REVIEW 1 cited by
Enhancing Phishing Email Identification with Large Language Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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.
Forward citations
Cited by 1 Pith paper
-
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 ...
Discussion (0). Continue with ORCID to comment.