Secure Data Encryption in Energy Production and Management Systems: Integrating Chaos Bifurcation and Polynomial High Order Fibonacci for Enhanced Cybersecurity
Bibliographic record
Abstract
Secure data handling is paramount in energy production and management systems, where cyber threats pose significant risks to operational continuity.In response, this study proposes an integration of chaos bifurcation and the Polynomial High Order Fibonacci (PHOF) approach to fortify encryption protocols in critical energy infrastructures.The method combines polynomial-based Fibonacci sequences with chaotic iteration steps analyzed through bifurcation to generate non-linear keystreams.These keystreams deliver robust confusion and diffusion capabilities, effectively mitigating brute-force and statistical attacks.Experimental findings confirm substantial gains in randomness, validated by entropy assessments and avalanche effect tests.Moreover, chaos bifurcation analysis highlights the sensitivity of the system's chaotic parameters, reinforcing security under varying conditions.Despite these layered mechanisms, the PHOF-chaotic scheme maintains a low computational burden, making it highly suitable for real-time data exchange within energy monitoring and control frameworks.Consequently, coupling PHOF with chaos bifurcation techniques significantly strengthens cybersecurity for energy systems, ensuring both reliable performance under operational demands and resilient protection against evolving cyber threats.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".