Chaos-based Pseudo Random Number Generators via quasi-synchronized Chua’s circuits: a symmetric encryption perspective
Bibliographic record
Abstract
Abstract Research efforts have extensively shown the significant roles of unpredictable phenomena originated in nature, such as chaos, as an inspiring discipline to generate random numbers. In the current study, focusing on encryption purposes, the main strategy is to provide a keystream using Pseudo Random Number Generators (PRNGs) derived from synchronized chaotic systems. Numerous Investigations performed on the synchronized configuration of Chua’s circuit have proven the advantage of its application as a prominent chaotic system. However, the less than ideal alignment between the core objective of the research, that is offering a secure communication optionally necessitating an arbitrary distance of the correspondents, and the standard version of synchronized Chua’s circuits not fulfilling this condition pragmatically, made the authors tackle the problem analytically by using the governing equations of the circuits and creating the so-called quasi-synchronized condition between the receiving and the transmitting sides. At last, eight different mathematical schemes are designed to manipulate the numerical data provided by the Chua’s equations to generate binary sequences for encryption purposes and then the main attempt has been dedicated to the evaluation of the results and finding the optimal PRNGs by conventional standards in cryptography provided by National Institute of Standards and Technology. Finally, three of the designed schemes are chosen as the successful methods, passing randomness criteria.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".