Quantum Communication Protocols to Improve the Security and Reliability of Next-Generation Smart Energy Systems
Bibliographic record
Abstract
Smart energy systems are expediting the development of IoT-based devices, distributed energy resources and smart grid operations. This has eased more efficiency and environmental benefits. The digital systems lack privacy, accuracy, and reliability of data, thus making it susceptible to hacking and other evil acts. Even though it is resistant to most of the attacks, traditional cryptography may not work anymore as computers accelerate and quantum computers are put into practice. One can also utilize quantum communication technologies which are based on quantum physics which is more secure. BB84 and E91 algorithm of quantum key distribution in next-generation smart energy systems are the ones used in the current paper. The suggested solutions contribute to the improved security and stability of the operation as they bring the laws of quantum entanglement and superposition in line with the needs of the energy sector. The paper also outlines the major ways through which smart grids can be hacked like manipulating data, using false information and denying-ofservice and how they can be minimized by employing quantumenhanced communication. The success rate and level of data integrity of 98.7 % and 97.6 respectively are better than the classical cryptography and blockchain-only systems by 7.3 and 5.3 % respectively, as indicated by simulation analysis. The hybrid framework demonstrates the resilience against the false data injection and denial-of-service attacks up to 17 %.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".