Paper Citation Record · LEDGER

Enhancing Phishing Email Identification with Large Language Models

As of 10 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2502.04759.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.04759 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T21:38:52.611296Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:32:37.357426Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-07T00:32:37.714289Z

Reference resolution

42 of 42 outbound references displayed

verified exact21
verified fuzzy8
unresolved11
parse uncertain0
malformed identifier2
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 60e04779-44cd-4397-a46f-3ef83f315dfb · outbound

This paper cites an unresolved cited work.

Enhancing Phishing Email Identification with Large Language Models Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-08T21:38:54.389833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d1bee15e-cbc1-4199-ad5a-5029ea1732a5 · outbound

This paper cites Hybrid Feature Selection for Phishing Email Detection,.

Enhancing Phishing Email Identification with Large Language Models Hybrid Feature Selection for Phishing Email Detection,

Reference 2

Resolution: verified exact; doi, observed 2026-08-08T21:38:52.748661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c2df827e-6f87-434b-9268-29ee174c2d0b · outbound

This paper cites Generative Adversarial Networks for Synthetic Training Data Replacement in Phishing Email Detection Using Natural Language Processing,.

Enhancing Phishing Email Identification with Large Language Models Generative Adversarial Networks for Synthetic Training Data Replacement in Phishing Email Detection Using Natural Language Processing,

Reference 3

Resolution: malformed identifier; no resolver link, observed 2026-08-08T21:38:52.492849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3df5a281-efde-4b17-81c2-5d094f29614d · outbound

This paper cites Phishing email detection technique by using hybrid features,.

Enhancing Phishing Email Identification with Large Language Models Phishing email detection technique by using hybrid features,

Reference 4

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T21:38:54.300822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3efe3ac3-30ea-445e-8fb9-f641cad82dae · outbound

This paper cites A Comparison of Natural Language Processing and Machine Learning Methods for Phishing Email Detection,.

Enhancing Phishing Email Identification with Large Language Models A Comparison of Natural Language Processing and Machine Learning Methods for Phishing Email Detection,

Reference 5

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T21:38:54.048618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b59aebb5-e0b6-4022-ad24-19530eac2880 · outbound

This paper cites Large Language Models in Cybersecurity: State-of-the-Art.

Enhancing Phishing Email Identification with Large Language Models Large Language Models in Cybersecurity: State-of-the-Art

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-08T21:38:52.502655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82c3e772-1b8a-4a54-a4a2-fc0d1617c7df · outbound

This paper cites Extended Abstract: Assessing Language Models for Semantic Textual Similarity in Cybersecurity,.

Enhancing Phishing Email Identification with Large Language Models Extended Abstract: Assessing Language Models for Semantic Textual Similarity in Cybersecurity,

Reference 7

Resolution: verified exact; doi, observed 2026-08-08T21:38:52.734829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 367f627e-1447-4b6b-bc4b-d636f570c682 · outbound

This paper cites A machine learning approach towards phishing email detection CEN - Security@IWSPA 2018,.

Enhancing Phishing Email Identification with Large Language Models A machine learning approach towards phishing email detection CEN - Security@IWSPA 2018,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:38:54.380307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 93329373-3064-48fa-9808-20c3daf7aac6 · outbound

This paper cites Cyber Threat Hunting Using Large Language Models,.

Enhancing Phishing Email Identification with Large Language Models Cyber Threat Hunting Using Large Language Models,

Reference 9

Resolution: verified exact; doi, observed 2026-08-08T21:38:52.726020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 83e38e80-0a4b-4988-aaf2-a594b44777bc · outbound

This paper cites GitHub - rf-peixoto/phishing_pot: A collection of phishing samples for researchers and detection developers.,.

Enhancing Phishing Email Identification with Large Language Models GitHub - rf-peixoto/phishing_pot: A collection of phishing samples for researchers and detection developers.,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:38:54.370777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9735a314-9731-444b-ade2-0a5d95ec398f · outbound

This paper cites A Systematic Review of Deep Learning Techniques for Phishing Email Detection,.

Enhancing Phishing Email Identification with Large Language Models A Systematic Review of Deep Learning Techniques for Phishing Email Detection,

Reference 11

Resolution: verified exact; doi, observed 2026-08-08T21:38:52.717190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7f5f8659-13fe-449c-b7fc-6eea32dc00a8 · outbound

This paper cites How Spammers are Abusing Twitter’s t.co URL Shortener | Cloudmark EN,.

Enhancing Phishing Email Identification with Large Language Models How Spammers are Abusing Twitter’s t.co URL Shortener | Cloudmark EN,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:38:54.361030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6f072247-2749-41ca-8584-c9f0e1955fc9 · outbound

This paper cites HELPHED: Hybrid Ensemble Learning PHishing Email Detection,.

Enhancing Phishing Email Identification with Large Language Models HELPHED: Hybrid Ensemble Learning PHishing Email Detection,

Reference 13

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T21:38:53.854016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d844d289-c844-4ab4-9e9c-c90880f715db · outbound

This paper cites Phishing Email Detection Using Natural Language Processing Techniques: A Literature Survey,.

Enhancing Phishing Email Identification with Large Language Models Phishing Email Detection Using Natural Language Processing Techniques: A Literature Survey,

Reference 14

Resolution: verified exact; doi, observed 2026-08-08T21:38:52.708288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 50d14c37-71c3-4951-8cf3-06f8d2f7f279 · outbound

This paper cites From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy,.

Enhancing Phishing Email Identification with Large Language Models From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy,

Reference 15

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T21:38:53.665117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ad5ac8b3-626d-4722-a6db-dc2ec1d839dc · outbound

This paper cites Phishing Attacks: A Recent Comprehensive Study and a New Anatomy,.

Enhancing Phishing Email Identification with Large Language Models Phishing Attacks: A Recent Comprehensive Study and a New Anatomy,

Reference 16

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T21:38:53.456546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 01c8b400-b68a-4f41-b502-2c9f612a6d03 · outbound

This paper cites How effective are large language models in detecting phishing emails?.

Enhancing Phishing Email Identification with Large Language Models How effective are large language models in detecting phishing emails?

Reference 17

Resolution: verified exact; doi, observed 2026-08-08T21:38:52.699393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5f72329d-e802-4a6f-a6d9-8a97937fb47d · outbound

This paper cites A Systematic Review on Deep -Learning-Based Phishing Email Detection,.

Enhancing Phishing Email Identification with Large Language Models A Systematic Review on Deep -Learning-Based Phishing Email Detection,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-08T21:38:52.538431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b98966ba-a707-4a83-9ece-710ee1974b62 · outbound

This paper cites A Systematic Literature Review on Phishing Email Detection Using Natural Language Processing Techniques,.

Enhancing Phishing Email Identification with Large Language Models A Systematic Literature Review on Phishing Email Detection Using Natural Language Processing Techniques,

Reference 19

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T21:38:53.287129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1d2401a4-ecdc-4bd9-aa60-469764bec649 · outbound

This paper cites Phishing and Social Engineering in the Age of LLMs,.

Enhancing Phishing Email Identification with Large Language Models Phishing and Social Engineering in the Age of LLMs,

Reference 20

Resolution: malformed identifier; no resolver link, observed 2026-08-08T21:38:52.544249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c32dcd84-0721-4af8-b3d8-d76e9b03aaa8 · outbound

This paper cites Large Multimodal Agents for Accurate Phishing Detection with Enhanced Token Optimization and Cost Reduction.

Enhancing Phishing Email Identification with Large Language Models Large Multimodal Agents for Accurate Phishing Detection with Enhanced Token Optimization and Cost Reduction

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-08T21:38:53.139751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 969d7276-7cc1-4712-b903-b01ab00b9bc0 · outbound

This paper cites Large Language Models Meet NLP: A Survey.

Enhancing Phishing Email Identification with Large Language Models Large Language Models Meet NLP: A Survey

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-08T21:38:52.550310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9f46fc5-1e55-474d-8594-b17a4ce7f2d0 · outbound

This paper cites GPT-4 Technical Report.

Enhancing Phishing Email Identification with Large Language Models GPT-4 Technical Report

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-08T21:38:52.553257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf7d636c-65e2-44ff-881f-aeb890c72fb6 · outbound

This paper cites Enhancing phishing email detection with stylometric features and classifier stacking,.

Enhancing Phishing Email Identification with Large Language Models Enhancing phishing email detection with stylometric features and classifier stacking,

Reference 24

Resolution: verified exact; doi, observed 2026-08-08T21:38:52.673126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e088fc88-175c-4cb7-abc8-7989172af687 · outbound

This paper cites How Good Are We at Detecting a Phishing Attack? Investigating the Evolving Phishing Attack Email and Why It Continues to Successfully Deceive Society,.

Enhancing Phishing Email Identification with Large Language Models How Good Are We at Detecting a Phishing Attack? Investigating the Evolving Phishing Attack Email and Why It Continues to Successfully Deceive Society,

Reference 25

Resolution: verified exact; doi, observed 2026-08-08T21:38:52.664433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ce5b6093-2db7-4110-bac0-625c3323b9fa · outbound

This paper cites Improving Phishing Email Detection Using the Hybrid Machine Learning Approach,.

Enhancing Phishing Email Identification with Large Language Models Improving Phishing Email Detection Using the Hybrid Machine Learning Approach,

Reference 26

Resolution: verified exact; doi, observed 2026-08-08T21:38:52.655277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f0ba3346-132e-46fa-aeb5-d0114c248a1d · outbound

This paper cites Detecting Scams Using Large Language Models.

Enhancing Phishing Email Identification with Large Language Models Detecting Scams Using Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-08T21:38:52.565526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c573979f-9b5a-4e0f-a172-efd690ec4f38 · outbound

This paper cites ChatSpamDetector: Leveraging Large Language Models for Effective Phishing Email Detection.

Enhancing Phishing Email Identification with Large Language Models ChatSpamDetector: Leveraging Large Language Models for Effective Phishing Email Detection

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-08T21:38:52.568993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e909260b-cafe-4bac-9258-69ec67e07346 · outbound

This paper cites Detecting Phishing URLs Based on a Deep Learning Approach to Prevent Cyber -Attacks,.

Enhancing Phishing Email Identification with Large Language Models Detecting Phishing URLs Based on a Deep Learning Approach to Prevent Cyber -Attacks,

Reference 29

Resolution: verified exact; doi, observed 2026-08-08T21:38:52.645946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fb2dd036-4f1f-4a62-b0c4-16a3c7db53c8 · outbound

This paper cites Prompted Contextual Vectors for Spear-Phishing Detection,.

Enhancing Phishing Email Identification with Large Language Models Prompted Contextual Vectors for Spear-Phishing Detection,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:38:54.351410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 76855b3c-a213-4267-a009-fcf73a48cdd2 · outbound

This paper cites Phishing Email Data by Type,.

Enhancing Phishing Email Identification with Large Language Models Phishing Email Data by Type,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:38:54.341735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ff25876a-1fdd-47db-8f93-d8159234254c · outbound

This paper cites Email Spam,.

Enhancing Phishing Email Identification with Large Language Models Email Spam,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:38:54.332540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d09f754c-1e1f-4ce5-8f52-4355fdba2d17 · outbound

This paper cites (2008), CLAIR collection of fraud email, ACL Data and Code Repository, ADCR2008T001, http://aclweb.org/aclwiki.

Enhancing Phishing Email Identification with Large Language Models (2008), CLAIR collection of fraud email, ACL Data and Code Repository, ADCR2008T001, http://aclweb.org/aclwiki

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:38:54.322502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8cf0bfc2-ebb6-423f-afb7-5b0a0a23730a · outbound

This paper cites Human -LLM generated phishing -legitimate emails,.

Enhancing Phishing Email Identification with Large Language Models Human -LLM generated phishing -legitimate emails,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:38:54.310701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0110709d-6fa2-4894-9380-d58268198f6d · outbound

This paper cites Microsoft Ignite: Redefining email security with LLMs to tackle a new era of social engineering,.

Enhancing Phishing Email Identification with Large Language Models Microsoft Ignite: Redefining email security with LLMs to tackle a new era of social engineering,

Reference 35

Resolution: verified exact; raw_fallback, observed 2026-08-08T21:38:53.096669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2a30315c-cb69-4e2a-9ce3-8168ea2d4d83 · outbound

This paper cites Devising and Detecting Phishing: Large Language Models vs. Smaller Human Models.

Enhancing Phishing Email Identification with Large Language Models Devising and Detecting Phishing: Large Language Models vs. Smaller Human Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-08T21:38:52.595834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50f4d405-0cc8-4853-a9ad-fe6bb43fff52 · outbound

This paper cites From ML to LLM: Evaluating the Robustness of Phishing Webpage Detection Models against Adversarial Attacks.

Enhancing Phishing Email Identification with Large Language Models From ML to LLM: Evaluating the Robustness of Phishing Webpage Detection Models against Adversarial Attacks

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-08-08T21:38:52.963749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation df9ce038-5fc0-455c-993d-a9513aeac921 · outbound

This paper cites Novel Interpretable and Robust Web-based AI Platform for Phishing Email Detection.

Enhancing Phishing Email Identification with Large Language Models Novel Interpretable and Robust Web-based AI Platform for Phishing Email Detection

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-08T21:38:52.952203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6e5051c7-9d37-42b6-b551-e9315cc8f41e · outbound

This paper cites Anomaly Detection in Emails using Machine Learning and Header Information.

Enhancing Phishing Email Identification with Large Language Models Anomaly Detection in Emails using Machine Learning and Header Information

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-08T21:38:52.605117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97ad011b-724d-48e5-af73-e605bd1f3c29 · outbound

This paper cites Machine learning based phishing detection from URLs,.

Enhancing Phishing Email Identification with Large Language Models Machine learning based phishing detection from URLs,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-08T21:38:52.608455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23870946-e106-4c62-acfa-316591cd1d5f · outbound

This paper cites Machine Learning Algorithms Evaluation for Phishing URLs Classification,.

Enhancing Phishing Email Identification with Large Language Models Machine Learning Algorithms Evaluation for Phishing URLs Classification,

Reference 41

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T21:38:52.932664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 318694d3-a46e-4333-916b-47e8d99d27e7 · outbound

This paper cites Prompted Contextual Vectors for Spear-Phishing Detection.

Enhancing Phishing Email Identification with Large Language Models Prompted Contextual Vectors for Spear-Phishing Detection

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-08T21:38:52.578308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation d61cce47-d4c1-4893-91f7-83e299df1900 · inbound

Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability cites this paper.

Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability Enhancing Phishing Email Identification with Large Language Models

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-07T00:32:37.719145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.