A Hybrid Approach for a Secured Information Security Using Modified Encryption Technique
Bibliographic record
Abstract
This paper proposes a new approach called the RAES technique, which results from redesigning the current Rail Fence Cipher (RFC) using two basic phases, first using the Advanced Encryption Standard (AES) technique and then using the potential of the RFC technique to protect confidential messages for more secure information security. There are several conventional cryptographic methods, and because it is possible to crack cipher text, that is why it tries to suggest RAES techniques written in C++ programming to be more secure to protect information from cipher breaking. Mixing RFC ciphers with AES, it appears that the encryption and decryption of the modified RAES require the generation of the plaintext elements which are usually single letters written in a predetermined sequence into a matrix format which is basically a rectangle that has been decided by the transmitter and receiver in advance, and then it is read off according to another predetermined sequence across the matrix to get the cipher text. Through this RAES technique, not only the strength of the AES technique can be applied but also the RFC technique that uses keywords and salt can also be used making this mixed system perform ciphers that are difficult to break by attackers. Moreover, the strength of the RAES algorithm is in terms of faster and more secure execution times than existing substitution and transposition algorithms in addition to the improvement of confusion and diffusion characteristics. Meanwhile, the value of the avalanche effect for the RAES technique recorded also showed that it reached 60%.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".