Application of Cryptography and Steganography Techniques to Improving the Security of Text Messages with RC4 Algorithm and MSB Method
Bibliographic record
Abstract
This study discusses the application of cryptography and steganography techniques to improve the security of text messages using the Rivest Code 4 (RC4) algorithm and the Most Significant Bit (MSB) method. In the ever-growing digital era, data security is a top priority due to the increasing threat of cybercrime that can harm many parties. RC4 is a cryptography algorithm known for its encryption and decryption speed, while the MSB method is an effective steganography technique for hiding information in digital images. This study aims to develop an application that is able to encrypt text messages with the RC4 algorithm and hide them in digital images using the MSB method. With this combination, data is not only encrypted but also hidden, thus providing two layers of security to protect information from unauthorized access. The results of the study show that the combination of RC4 cryptography and MSB steganography techniques successfully improves data security well. The developed application is able to protect sensitive information from the risk of data theft and cyber attacks. In addition, this technique is also easy to implement and can be applied in various sectors, such as banking, health, and business communications, to protect sensitive data from unauthorized access. Keywords: Cryptography, Steganography, Most Significant Bit (MSB)
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".