Application of Modified Vigenere Cipher Cryptography Technique in Text Data Encryption in Web Based Applications
Bibliographic record
Abstract
The development of information technology in today's digital era has brought rapid progress, especially in terms of data security which is a major concern in various sectors. In securing data or information, the application of cryptographic techniques is becoming increasingly important. The vigenere cipher algorithm is one of the classic cryptographic methods widely used for text encryption. However, the standard form of encryption algorithm has weaknesses, especially in terms of its vulnerability to frequency analysis attacks that can be used to break the encryption pattern if the key used is repeated. This research aims to design and implement a modified Vigenere cipher on a web-based application to improve encryption security. The designed web-based application is a password manager that is used as a test to show what the application of the vigenere cipher modification looks like in an application. The password manager application designed has the features of registering, logging in, storing, managing, and securing various passwords and login information for websites, applications, or online services. sensitive data in this application such as login information is encrypted before being stored in the database and decrypted when displayed to the user.This implementation is expected to show the application of the Vigenere Cipher modification in securing data in web-based applications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".