Enhanced Security Through Integrated Morse Code Encryption and LSB Steganography in Digital Communications
Bibliographic record
Abstract
In the digital era, the protection of sensitive data transmitted over the Internet, such as credit card information, is paramount.This study introduces an innovative system that enhances data security during internet transmission by synergizing steganography and cryptography.The proposed method employs a two-stage process: encryption followed by concealment.Initially, secret text data is encrypted using an evolved form of Morse code, converting it into ciphertext.Subsequently, this ciphertext is discreetly embedded within a cover image utilizing the least significant bit (LSB) technique, a method renowned for its subtlety and efficiency in data hiding.The effectiveness of this novel system was evaluated by comparing its performance with existing benchmarks.The chosen test case involved embedding encrypted data within a Baboon image.The results demonstrated a notable improvement of 2.596% over the baseline, affirming the system's efficacy.A critical aspect of this approach is the high quality of the resultant stego image.This quality is instrumental in ensuring the covert nature of the embedded data, thereby significantly reducing the likelihood of detection during internet transmission.Key elements of this study include the development of a more sophisticated Morse code encryption algorithm and the optimization of the LSB steganography technique.These advancements contribute to the system's robustness, rendering the encrypted and hidden data virtually undetectable and inaccessible to unauthorized entities.The integration of these two techniques represents a significant stride in the realm of digital data security, offering a dual-layered defense mechanism against potential cyber threats.
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.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".