The Passwordless Authentication with Passkey Technology from an Implementation Perspective
Bibliographic record
Abstract
With the rise of sophisticated authentication bypass techniques, passwords are no longer considered a reliable method for securing authentication systems. In recent years, new authentication technologies have shifted from traditional password-based logins to passwordless security. Among these, Time-Based One-Time Passwords (TOTP) remain one of the most widely used mechanisms, while Passkeys are emerging as a promising alternative with growing adoption. This paper highlights the key techniques used during the implementation of the authentication system with Passkey technology. It also suggests considerations for integrating components during system development to ensure that users can securely access their accounts with minimal complexity, while still meeting the requirements of a robust authentication system that balances security, usability, and performance. Additionally, by examining TOTP and Passkey mechanisms from an implementation perspective, this work not only addresses major security concerns such as password leaks, phishing attacks, and susceptibility to bruteforce attacks, but also evaluates the feasibility and effectiveness of these mechanisms in real-world implementations. This paper demonstrates the superior security of Passkey technology and its potential for broader adoption in secure authentication systems.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".