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Record W4383228282 · doi:10.31219/osf.io/6f2y9

Phishing Vulnerability Compounded by Older Age, APOE4 Genotype, and Lower Cognition

2023· preprint· en· W4383228282 on OpenAlexaff
Didem Pehlivanoglu, Alayna Shoenfelt, Ziad M Hakim, Amber Heemskerk, Jialong Zhen, Mario Mosqueda, Robert C. Wilson, Matthew J. Huentelman, Matthew D Grilli, Gary R. Turner, R. Nathan Spreng, Natalie C. Ebner

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicSpam and Phishing Detection
Canadian institutionsMcGill UniversityYork University
FundersNational Institute on AgingNational Institutes of HealthFlorida Department of Health
KeywordsPhishingCognitionPsychologyInternet privacyThe InternetGerontologyComputer securityComputer scienceMedicineWorld Wide WebPsychiatry

Abstract

fetched live from OpenAlex

With technological advancements, financial exploitation tactics have expanded into the online realm. Older adults may be particularly susceptible to online scams due to age- and Alzheimer’s Disease-related changes in cognition. In this study, 182 adults ranging from 18-90 years underwent cognitive assessment, genotyping for apolipoprotein E e4 (APOE4), and completed the lab-based Short Phishing Email Suspicion Test (S-PEST) as well as the real-life PHishing Internet Task (PHIT). Across both paradigms, older age predicted heightened susceptibility to phishing, with this enhanced susceptibility pronounced among older APOE4 allele carriers with lower working memory. Additionally, performance in both phishing tasks was correlated, in that reduced ability to discriminate between phishing and safe emails in S-PEST predicted greater phishing susceptibility in PHIT. The current study introduces S-PEST as an easy-to-administer, ecologically valid tool for assessing phishing susceptibility and identifies older age, APOE4, and lower cognition as risk factors of phishing vulnerability.

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 categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.859
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.041
GPT teacher head0.277
Teacher spread0.237 · 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.

Study designTheoretical or conceptual
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

Citations6
Published2023
Admission routes1
Has abstractyes

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