Strong Password Generation Based On User Inputs
Bibliographic record
Abstract
Every person using different online services is concerned with the security and privacy for protecting individual information from the intruders. Many authentication systems are available for the protection of individuals' data, and the password authentication system is one of them. Due to the increment of information sharing, internet popularization, electronic commerce transactions, and data transferring, both password security and authenticity have become an essential and necessary subject. But it is also mandatory to ensure the strength of the password. For that reason, all cyber experts recommend intricate password patterns. But most of the time, the users forget their passwords because of those complicated patterns. In this paper, we are proposing a unique algorithm that will generate a strong password, unlike other existing random password generators. This password will he based on the information, i.e. (some words and numbers) provided by the users so that they do not feel challenged to remember the password. We have tested our system through various experiments using synthetic input data. We also have checked our generator with four popular online password checkers to verify the strength of the produced passwords. Based on our experiments, the reliability of our generated passwords is entirely satisfactory. We also have examined that our generated passwords can defend against two password cracking attacks named the "Dictionary attack" and the "Brute Force attack". We have implemented our system in Python programming language. In the near future, we have a plan to extend our work by developing an online free to use user interface. The passwords generated by our system are not only user-friendly but also have achieved most of the qualities of being strong as well as non- crackable passwords.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".