Bibliographic record
Abstract
We use the cognitive science concept of mental models as a lens to identify issues relating to security in user interfaces. We speculate that more could be done by system designers to help users better understand the security-relevant aspects of the software they use, which would enable better-informed (and hopefully safer) behaviour. This thesis presents studies and tools for helping system designers do so. The mental models literature posits that the ability to act successfully depends on one's understanding of how things in the world work, and with richer experience comes better understanding. Prior work has shown there are issues with user mental models of cybersecurity that can lead to unsafe behaviour. This is corroborated by our first study, in which we compare participant drawings of malware and regular software, and finding dangerous inaccuracies with implications for behaviour. The majority of this thesis then consists of the development and presentation of two conceptual models for improving support for security-related information in UIs, and their applications. The first is the 'Home-Away' model: a pattern of contexts and behaviours that help people protect valuable items while managing attentional resources. This model is established over the course of three studies: a focus group where the pattern was first discovered, and two follow-up studies which corroborated the early findings. We then identify a set of UI Design patterns that enable users to apply existing 'Home-Away' tendencies in less-familiar cybersecurity contexts. We then review the general relationship between UI design and security understanding, and propose the Security Awareness Visibility and Evaluation (SAVE) as a new paradigm for UI design that keeps users informed of relevant security issues without overburdening them. We design and implement a prototype SAVE application that communicates key information from web certificates to users to help them identify fraudulent websites, which we call Site Inspector. We test this prototype with users, finding encouraging preliminary signs of its effectiveness.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".