RoundPIN: Shoulder Surfing Resistance for PIN Entry with Randomize Keypad
Bibliographic record
Abstract
The security of a PIN is largely supported by the authentication process in ATM. Most authentication methods like traditional are based on using PIN as direct entry and this technique has been shown lots of drawbacks such as vulnerability to password space, and shoulder-surfing. In this paper, a new approach is proposed called RoundPIN depends on the appearance of the numerical password through one of the buttons after selecting it by the user and it is done through a number of rounds, the numbers are arranged randomly on the keypad. Due to the variable aspect of the chosen button and the random appearance of the numbers in each connection session and also the selection process will take place through three buttons three auxiliary, the proposed approach can maintain high secure session to enter the PIN to resist shoulder surfing, which is difficult for attackers to observe a user's PIN. The performance evaluation of the proposed approach is achieved in two parts, the first one is based on security analysis. Then a pilot study of thirty users is conducted to evaluate the useability of the proposed approach. It is noticed that the proposed approach can maintain a high level of security as well as acceptable level of useability and user satisfaction compared the conventional keypad system.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".