On the public citation graph

Accuracy, Feature Interpretability, and Usability of LLMs in Phishing Email Detection

0 signed · 14 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s11063-017-9593-7openalex-snapshot
Optimization Based Clustering Algorithms for Authorship Analysis of Phishing Emails
inferredcites →https://doi.org/10.1007/s11235-017-0334-zopenalex-snapshot
Defending against phishing attacks: taxonomy of methods, current issues and future directions
inferredcites →https://doi.org/10.1109/access.2022.3151903openalex-snapshot
Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions
inferredcites →https://doi.org/10.1109/bigdata62323.2024.10825007openalex-snapshot
Generating Phishing Attacks and Novel Detection Algorithms in the Era of Large Language Models
inferredcites →https://doi.org/10.1109/ccaa.2016.7813778openalex-snapshot
A literature survey on social engineering attacks: Phishing attack
inferredcites →https://doi.org/10.1109/ccwc60891.2024.10427626openalex-snapshot
Can AI Keep You Safe? A Study of Large Language Models for Phishing Detection
inferredcites →https://doi.org/10.1109/dsc63325.2024.00017openalex-snapshot
Enhancing Cybersecurity with Transformers: Preventing Phishing Emails and Social Media Scams
inferredcites →https://doi.org/10.1109/surv.2013.030713.00020openalex-snapshot
A Survey of Phishing Email Filtering Techniques
inferredcites →https://doi.org/10.1109/tifs.2019.2902822openalex-snapshot
Unsupervised Machine Learning-Based Detection of Covert Data Integrity Assault in Smart Grid Networks Utilizing Isolation Forest
inferredcites →https://doi.org/10.1142/s0218213019300023openalex-snapshot
Machine Learning and Nature Inspired Based Phishing Detection: A Literature Survey
inferredcites →https://doi.org/10.1145/1299015.1299021openalex-snapshot
A comparison of machine learning techniques for phishing detection
inferredcites →https://doi.org/10.18653/v1/2025.nlp4dh-1.14openalex-snapshot
PsyTEx: A Knowledge-Guided Approach to Refining Text for Psychological Analysis
inferredcites →https://doi.org/10.3233/jcs-181253openalex-snapshot
How persuasive is a phishing email? A phishing game for phishing awareness
inferredcites →https://doi.org/10.3390/make6010018openalex-snapshot
Prompt Engineering or Fine-Tuning? A Case Study on Phishing Detection with Large Language Models
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.