Towards Secure Design Choices for Implementing Graphical Passwords
Bibliographic record
Abstract
We study the impact of selected parameters on the size of the password space for "Draw-A-Secret" (DAS) graphical passwords. We examine the role of and relationships between the number of composite strokes, grid dimensions, and password length in the DAS password space. We show that a very significant proportion of the DAS password space depends on the assumption that users will choose long passwords with many composite strokes. If users choose passwords having 4 or fewer strokes, with passwords of length 12 or less on a 5 /spl times/ 5 grid, instead of up to the maximum 12 possible strokes, the size of the DAS password space is reduced from 58 to 40 bits. Additionally, we found a similar reduction when users choose no strokes of length 1. To strengthen security, we propose a technique and describe a representative system that may gain up to 16 more bits of security with an expected negligible increase in input time. Our results can be directly applied to determine secure design choices, graphical password parameter guidelines, and in deciding which parameters deserve focus in graphical password user studies.
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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 | 0.005 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".