SE-CDR: Enhancing Security and Efficiency of Key Management in Internet of Energy Consumer Demand-Response Communications
Bibliographic record
Abstract
The burgeoning Internet of Energy (IoE) paradigm, a fusion of the Internet of Things (IoT) and Smart Grid (SG) technologies, holds the promise of significantly enhancing the reliability and efficiency of energy production, transmission, and consumption across the entire energy chain, from generation to the end user.Two central technical aspects that enable this innovation are the advent of smart consumer electronics and the establishment of bidirectional IoT communications.These developments have facilitated the incorporation of novel applications into the Smart Grid, including smart metering, Consumer Demand-Response (CDR) management, and prepayment.In this study, our focus lies primarily on the development of a secure and efficient key management system for CDR communications.It is demonstrated herein that a previous key graph-based scheme, called EDR, is susceptible to collusion attacks and lacks support for broadcast CDR communications.In response to these vulnerabilities, we propose a novel key management scheme, referred to as Secure and Efficient key management scheme for CDR communications (SE-CDR).This scheme retains the strengths of the EDR while introducing a modified multi-group key graph technique, designed to ensure the secure, efficient, and scalable management of unicast, multicast, and broadcast CDR communications.The presented security analysis and performance evaluation results establish the robust security of the SE-CDR scheme.Moreover, a comparative analysis revealed that this new approach offers significant improvements in terms of storage and communication efficiency, outperforming existing state-of-the-art methods.This study thus presents a promising advancement in the realm of secure and efficient key management for the Internet of Energy paradigm.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".