A Hybrid Approach for a Secured Information Security Using Modified Encryption Technique
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it