A Hybrid Post-Quantum Secure Framework Integrating McEliece KEM, RC6 Encryption and Steganography for IoT Data Protection
Bibliographic record
Abstract
The dramatic growth in the number of digital data which are exchanged combined with the increased complexity of cyber threats requires a system which is strong enough to provide the data confidentiality and integrity. The classical steganographic and cryptography are only adequate in the context of solitude, and not in the context of imperceptibility and resistance to sophisticated attacks. An adaptive cryptography system augmented with clever steganography is suggested to address these weaknesses. The system has been designed with three layers: (i) dynamic cryptographic layer, which uses McEliece Key Encapsulation with noise thresholds to provide post-quantum resilience, (ii) adaptive encryption layer using RC6 with a threshold parameter to use for statistical diffusion, and (iii) steganographic embedding layer using hybrid wavelet-domain embedding optimized using Particle Swarm Optimization (PSO) in order to achieve imperceptibility and robustness. The layered design guarantees protection against noise, compression, and cropping attacks and at the same time computational efficiency such that it can be used in IoT and health care tasks in real time. The effectiveness of the suggested system over the traditional LSB and DWT-GA approaches is proven by experimental evidence. It is worth noting that the whole hiding capacity of the framework reaches the highest signal-to-noise ratio (PSNR) of 39.8 dB, thus, meaning that, the content is greatly imperceptible at the integrity maintained. The model suggested therefore presents a secure, efficient, and scalable solution that is flexible enough to accommodate new digital ecosystems in which privacy of information and resiliency would be of utmost importance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".