Phishing detection in multitasking contexts: the impact of working memory load, goal activation, and message framing cue on detection performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Phishing, a prevalent cyber threat leveraging social engineering, poses significant challenges in the digital landscape. Despite advancements in security technologies, phishing continues to exploit human vulnerabilities, underscoring the need to understand how individuals detect such attacks. Existing research often assumes phishing detection occurs in isolation, overlooking real-world multitasking contexts where competing cognitive demands can hinder detection. This study fills this gap by examining phishing detection in the multitasking context and theorizing the relevant cognitive mechanisms and phishing-specific factors that influence phishing detection performance. Drawing on the memory-for-goals theory, we investigate the effects of working memory load (WML) from the primary task, goal activation (GA) towards phishing detection on performance, and message framing of phishing attacks. Findings from two online experiments reveal that increased WML impairs detection accuracy, while GA improves performance and reduces the negative impact of WML; furthermore, GA plays a more significant role in gain-framed phishing emails compared to loss-framed ones. Our research shifts the focus from message characteristics to the influence of multitasking on phishing detection. The results highlight the need for context-aware interventions that better align with real-world user behaviour, which provides a foundation for designing more effective phishing defences in multitasking digital environments.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it