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Record W4396839042 · doi:10.1145/3638380.3638434

A Large-Scale Study of Device and Link Presentation in Email Phishing Susceptibility

2023· article· en· W4396839042 on OpenAlexaff
Sijie Zhuo, Robert Biddle, Lucas Betts, Nalin Arachchilage, Yun Sing Koh, Danielle Lottridge, Giovanni Russello

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSpam and Phishing Detection
Canadian institutionsCarleton University
Fundersnot available
KeywordsPhishingPresentation (obstetrics)Computer scienceInternet privacyWorld Wide WebHypertextMasking (illustration)The InternetComputer securityMedicine

Abstract

fetched live from OpenAlex

Phishing is one of the most prevalent social engineering attacks that targets both organisations and individuals. It is crucial to understand how email presentation impacts users’ reactions to phishing attacks. We hypothesised that device type and email presentation could potentially play a role, particularly in how links are displayed, which might influence susceptibility. In collaboration with the IT Services unit of a large organisation for a phishing training exercise, we conducted a study to explore the effects of device type and link presentation. Our findings revealed no significant difference in users’ susceptibility to phishing when using mobile devices versus computers. However, the masking of phishing links as buttons or hypertext appeared to be influential in shaping users’ behaviour. More specifically, users were significantly more likely to click on phishing links when masked as hypertext. These findings suggest that link presentation plays a significant role in users’ susceptibility to phishing attacks.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.297
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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