DEVELOPMENT OF HYBRID ENCRYPTION METHOD USING AFFINE CIPHER, VIGENERE CIPHER, AND ELGAMAL ALGORITHM TO SECURE TEXT MESSAGES IN DATA COMMUNICATION SYSTEM
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
With the development of technological advances in this day and age, it definitely requires a security system on messages and data. The way to maintain the security of data, messages or information requires a branch of science in its application, one of which is the algorithm or cryptography method. In its application, it requires more than one stage of the security process, because data security can be done by combining methods in its security techniques. This research aims to develop encryption methods using Affine Cipher, Vigenere Cipher, and ElGamal Algorithm to secure text messages. Affine cipher, Vigenere cipher and ElGamal are cryptography that can encrypt and decrypt text messages. Encryption is changing the message or plaintext into an unreadable message or ciphertext, on the other hand, decryption changes the ciphertext or message that initially cannot be read into a message that can be read or plaintext back in its original form. The result of this research is the development stage by doing three encryption and decryption processes. For the first encryption process using Affine Chiper which produces the initial ciphertext, then re-encrypted using Vigenere Cipher, then the previous encryption results are carried out ElGamal encryption which produces the final ciphertext. Conversely, the decryption process is first on ElGamal, then Vigenere Cipher, and finally Affine Cipher whose decryption results in plaintext back in the form of the initial text message. So that by developing and combining three algorithm methods can increase the security of information and text messages.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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