Trust and Risk in Website Legitimacy and Software Applications: Delving Into User Understanding of Internet Security Mechanisms
Bibliographic record
Abstract
When people choose to visit a given website, they make a trust decision about the supplier and source.It appears that a large majority of users commonly place their trust in most, if not all, websites they encounter, and this causes significant security problems.Any solutions proposed to reduce the threat of malicious websites must include a consideration of the psychological processes of the end users.This thesis presents several studies with the aim of understanding how people interpret the available information when making a trust decision.This understanding will better support users in making appropriate decisions and should inform better design of security mechanisms.It was found that users show some understanding of some of the key concepts in Internet security, and often make reasonable decisions.However, there are important anomalies.For example, many users had important misunderstandings about malware, suggesting they had poor mental models about the capabilities of malware and the capabilities of antivirus software applications in protecting them from threats online.Moreover, participants showed lack of confidence across a range of issues, but in practice they were still willing to make decisions even with this uncertainty.Some evidence was found which suggests that users employ heuristics in making such decisions and judgments under uncertainty.Potential solutions to address this would include closed software markets with certificates, or improved design to help users build better mental models.Biddle, who spent an enormous amount of time with me going through the different sections that make up a thesis, mainly the planning, research, and writing of the thesis.He also devoted a lot of time helping me towards the end of the final write-up -including weekends and holidays -in order to meet the final deadline for final submission and defence.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".