Bibliographic record
Abstract
Online banking is one of the most sensitive tasks performed by general Internet users. Most traditional banks now offer online banking services, and strongly encourage customers to do online banking with 'peace of mind.' Although banks heavily advertise an apparent '100% online security guarantee,' typically the fine print makes this conditional on users fulfilling certain security requirements. We examine some of these requirements as set by major Canadian banks, in terms of security and usability. We opened personal checking accounts at the five largest Canadian banks, and one online-only bank. We found that many security requirements are too difficult for regular users to follow, and believe that some marketing-related messages about safety and security actually mislead users. We are also interested in what kind of computer systems people really use for online banking, and whether users satisfy common online banking requirements. Our survey of 123 technically advanced users from a university environment strongly supports our view of an emerging gap between banks' expectations (or at least what their written customer policy agreements imply) and users' actions related to security requirements of online banking. Our participants, being more security-aware than the general population, arguably makes our results best-case regarding what can be expected from regular users. Yet most participants failed to satisfy common security requirements, implying most online banking customers do not (or cannot) follow banks' stated end-user security requirements and guidelines. The survey also sheds light on the security settings of systems used for sensitive online transactions. This work is intended to spur a discussion on real-world system security and user responsibilities, in a scenario where everyday users are heavily encouraged to perform critical tasks over the Internet, despite the continuing absence of appropriate tools to do so.
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 | 0.019 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".