Paper Citation Record · LEDGER

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection

As of 12 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2412.02084.

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

pith.paper-citation-record.v1
2412.02084 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T23:52:56.581934Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

verified exact8
verified fuzzy11
unresolved22
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 377b79a5-f208-4fe3-ac68-673107fcf98c · outbound

This paper cites "How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection "How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T23:52:56.355793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94f0178c-2582-4b01-9c5c-e71fd7c07f7b · outbound

This paper cites Exquisite Analysis of Popular Machine Learning –Based Phishing Detection Techniques for Cyber Systems,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Exquisite Analysis of Popular Machine Learning –Based Phishing Detection Techniques for Cyber Systems,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T23:52:56.362065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e015c1f-63b9-453c-815e-09e35e3e9831 · outbound

This paper cites Explainable Artificial Intelligence Approaches: A Survey,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Explainable Artificial Intelligence Approaches: A Survey,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:52:58.432169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 33568b44-85ca-4459-8c07-703ba80f35f8 · outbound

This paper cites Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T23:52:56.378212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation faad5759-b660-488e-b8c8-24b62c6b5f7a · outbound

This paper cites Sok: Explainable machine learning for computer security applications,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Sok: Explainable machine learning for computer security applications,

Reference 5

Resolution: verified exact; raw_fallback, observed 2026-08-11T23:52:57.998639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 820d41da-e58f-4091-b701-575febacf87d · outbound

This paper cites ‘Why Should I Trust You?’: Explaining the Predictions of Any Classifier,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection ‘Why Should I Trust You?’: Explaining the Predictions of Any Classifier,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T23:52:56.388865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c94a7e14-56c0-4db3-bc17-a670b178baae · outbound

This paper cites Evaluating Explanation Methods for Deep Learning in Security.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Evaluating Explanation Methods for Deep Learning in Security

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-11T23:52:57.896236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 555321ac-7eb4-4cc9-b1b8-5aefbcb3ad52 · outbound

This paper cites Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T23:52:56.400983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1dd42d2-88c3-4ad1-a539-3714d642268a · outbound

This paper cites VORTEX : Visual phishing detectiOns aRe Through EXplanations,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection VORTEX : Visual phishing detectiOns aRe Through EXplanations,

Reference 9

Resolution: verified exact; doi, observed 2026-08-11T23:52:56.873065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 894bda04-1d5a-4e62-9a02-246acdd3b82f · outbound

This paper cites Intelligent explanation generation system for phishing webpages by employing an inference system,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Intelligent explanation generation system for phishing webpages by employing an inference system,

Reference 10

Resolution: metadata mismatch; raw_fallback, observed 2026-08-11T23:52:57.870136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 012771ef-e7c6-4dc1-b77f-50bf1121876f · outbound

This paper cites An Innovative Information Theory -based Approach to Tackle and Enhance The Transparency in Phishing Detection,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection An Innovative Information Theory -based Approach to Tackle and Enhance The Transparency in Phishing Detection,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:52:58.413733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation edf43956-eb17-47ef-b7d0-ddf03f3f4631 · outbound

This paper cites Explaining URL Phishing Detection by Glass Box Models,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Explaining URL Phishing Detection by Glass Box Models,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:52:58.397925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9b695947-d4c1-4260-9cc0-38a176411b8c · outbound

This paper cites Explainable Machine Learning for Bag of Words-Based Phishing Detection,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Explainable Machine Learning for Bag of Words-Based Phishing Detection,

Reference 13

Resolution: verified exact; doi, observed 2026-08-11T23:52:56.857591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 50f8de0f-097c-4c79-ac22-c62aa5d440cd · outbound

This paper cites Explanations in warning dialogs to help users defend against phishi ng attacks,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Explanations in warning dialogs to help users defend against phishi ng attacks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:52:58.381304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f799c8e3-5a45-4364-a6da-d1ed776bd749 · outbound

This paper cites An explainable AI model to help users avoid being victims of phishing attacks,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection An explainable AI model to help users avoid being victims of phishing attacks,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:52:58.359853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b1b6fff4-5ac5-4ba4-a45d-f54dd3aa48a9 · outbound

This paper cites The State of Phishing.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection The State of Phishing

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:52:58.339892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 81943d8b-911e-423d-90cc-60f5747612f1 · outbound

This paper cites Phishing Trends 2023.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Phishing Trends 2023

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:52:58.313779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 16f641eb-24ae-41e0-92b7-ff2f6eccd7f7 · outbound

This paper cites CERT strategy to deal with phishing attacks,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection CERT strategy to deal with phishing attacks,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T23:52:56.451767Z

Source-reported events for the cited work

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Observation d4e11a5c-f307-49d9-ad32-4b0484695ac4 · 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,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection How Good Are We at Detecting a Phishing Attack? Investigating the Evolving Phishing Attack Email and Why It Continues to Successfully Deceive Society,

Reference 19

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Source-reported events for the cited work

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Observation d13a7f73-db8e-476e-8cf4-0ecde27d94ee · outbound

This paper cites Scam Pandemic: How Attackers Exploit Public Fear through Phishing,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Scam Pandemic: How Attackers Exploit Public Fear through Phishing,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T23:52:56.462108Z

Source-reported events for the cited work

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Observation 304be813-b65d-4500-a4a7-7d80b901eba4 · outbound

This paper cites Techniques for interpretable machine learning,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Techniques for interpretable machine learning,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T23:52:56.466890Z

Source-reported events for the cited work

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Observation 1fd3d728-e3e6-4ae6-ae75-3319e6ce6f0d · outbound

This paper cites Explaining Explanations: An Overview of Interpretability of Machine Learning.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Explaining Explanations: An Overview of Interpretability of Machine Learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T23:52:56.471769Z

Source-reported events for the cited work

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Observation 986bae1d-3d11-44ea-a0ed-4205e7d8c717 · outbound

This paper cites InterpretML.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection InterpretML

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:52:58.278613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 948ddc30-4e18-43c1-b2a3-16c7520a894c · outbound

This paper cites Explainable AI: Interpreting, Explaining and Visualizing Deep Learning,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Explainable AI: Interpreting, Explaining and Visualizing Deep Learning,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:52:58.253552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 91bfeb16-6a52-41f4-b208-801d92e4a47c · outbound

This paper cites Understanding Global Feature Contributions With Additive Importance Measures,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Understanding Global Feature Contributions With Additive Importance Measures,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T23:52:56.487144Z

Source-reported events for the cited work

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Observation ad1b76fc-bd52-4010-a972-4c2ebc890e9d · outbound

This paper cites Unpack Local Model Interpretation for GBDT,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Unpack Local Model Interpretation for GBDT,

Reference 26

Resolution: verified exact; doi, observed 2026-08-11T23:52:56.763652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3a4b1f83-13d1-45ec-bb78-1c079c6255b8 · outbound

This paper cites Using Rule Extraction to Improve the Comprehensibility of Predictive Models,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Using Rule Extraction to Improve the Comprehensibility of Predictive Models,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T23:52:56.498388Z

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Observation 10ea286b-0360-4cf1-806a-7b1d5c5c7a03 · outbound

This paper cites General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T23:52:56.503008Z

Source-reported events for the cited work

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Observation 007bcd9a-38cb-444e-bc77-673eeb4abe80 · outbound

This paper cites Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T23:52:56.508484Z

Source-reported events for the cited work

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Observation 95515f4e-7954-4569-9e9c-b08e176aa7e2 · outbound

This paper cites The black box problem of AI in oncology,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection The black box problem of AI in oncology,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T23:52:56.513728Z

Source-reported events for the cited work

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Observation 84684ece-eeb8-46ed-ad4b-b76d1c743537 · outbound

This paper cites Faithful and Customizable Explanations of Black Box Models,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Faithful and Customizable Explanations of Black Box Models,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T23:52:56.518550Z

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Observation 4fdfb5e3-43ee-437d-9c51-2d210dd9e916 · outbound

This paper cites How Experts Detect Phishing Scam Emails,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection How Experts Detect Phishing Scam Emails,

Reference 32

Resolution: verified exact; doi, observed 2026-08-11T23:52:56.699926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 55434116-ec9d-4ed3-ba15-d1732ff329b6 · outbound

This paper cites Visualizing and Interpreting RNN Models in URL -Based Phishing Detection,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Visualizing and Interpreting RNN Models in URL -Based Phishing Detection,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T23:52:56.528443Z

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Observation 1ecd027b-a1d3-44e7-ab6a-fd0e87675c92 · outbound

This paper cites Definitions, methods, and applications in interpretable machine learning,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Definitions, methods, and applications in interpretable machine learning,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T23:52:56.533211Z

Source-reported events for the cited work

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Observation 159331e7-10b5-4d74-8ca9-891a496e3833 · outbound

This paper cites SoK: A Comprehensive Reexamination of Phishing Research From the Security Perspective,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection SoK: A Comprehensive Reexamination of Phishing Research From the Security Perspective,

Reference 35

Resolution: metadata mismatch; raw_fallback, observed 2026-08-11T23:52:57.385429Z

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Observation f150e91f-b0a1-4142-9958-fba05aef4189 · outbound

This paper cites Interpretability as Approximation: Understanding Black -Box Models by Decision Boundary,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Interpretability as Approximation: Understanding Black -Box Models by Decision Boundary,

Reference 36

Resolution: verified exact; doi, observed 2026-08-11T23:52:56.669668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a3d0fbaa-916f-4d60-8a8c-47b1f0813f48 · outbound

This paper cites Explainable Artificial Intelligence: A Review and Case Study on Model -Agnostic Methods,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Explainable Artificial Intelligence: A Review and Case Study on Model -Agnostic Methods,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T23:52:56.547786Z

Source-reported events for the cited work

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Observation 83029b13-cd11-4896-9049-95a44b2e8c7d · outbound

This paper cites Interpreting black -box models: a review on explainable artificial intelligence,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Interpreting black -box models: a review on explainable artificial intelligence,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-11T23:52:56.553185Z

Source-reported events for the cited work

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Observation 5689094f-dc88-42fa-8ff5-d54e2549eab3 · outbound

This paper cites Benchmarking and survey of explanation methods for black box models,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Benchmarking and survey of explanation methods for black box models,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T23:52:56.559354Z

Source-reported events for the cited work

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Observation e3038682-e0d1-4776-afea-3a237fd9c39d · outbound

This paper cites The Past, Present, and Prospe ctive Future of XAI: A Comprehensive Review,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection The Past, Present, and Prospe ctive Future of XAI: A Comprehensive Review,

Reference 40

Resolution: verified exact; doi, observed 2026-08-11T23:52:56.625489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e6657e46-3259-448d-9cfa-45c9748bbf36 · outbound

This paper cites Measures for explainable AI: Explanation goodness, user satisfaction, menta l models, curiosity, trust, and human-AI performance,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Measures for explainable AI: Explanation goodness, user satisfaction, menta l models, curiosity, trust, and human-AI performance,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-11T23:52:56.569958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0db61645-436b-451b-a7a0-22b7ea1c2dc1 · outbound

This paper cites Molnar, Interpretable Machine Learning: A Guide for Making Black Box Models Explainable , 2nd ed.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Molnar, Interpretable Machine Learning: A Guide for Making Black Box Models Explainable , 2nd ed

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:52:58.234055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 365b2908-301f-4d1f-adb0-024be3982797 · outbound

This paper cites Measuring Interpretability for Different Types of Machine Learning Models,.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Measuring Interpretability for Different Types of Machine Learning Models,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:52:58.214907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ce1602e4-ffb9-403c-b4a3-aafec71e7e7a · outbound

This paper cites Explainable Artificial Intelligence Approaches: A Survey.

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Explainable Artificial Intelligence Approaches: A Survey

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-11T23:52:56.372694Z

Source-reported events for the cited work

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Pith citing papers

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