Authentication of Smart Grid by Integrating QKD and Blockchain in SCADA Systems
Bibliographic record
Abstract
Information and Communication Technology (ICT) provides customers with utilities and smart grid solutions, enabling enhanced monitoring and control of energy management systems. This technology is poised to elevate the reliability, sustainability, and efficiency of future electric grids through the implementation of advanced metering infrastructure (AMI). However, current Supervisory Control and Data Acquisition (SCADA) systems lack trusted machine authentication in smart grid communications, leaving the electric grid vulnerable to cyberattacks via sophisticated network technologies such as wireless access points, sensors, routers, and gateways. Therefore, ensuring proper management of data integrity from field sensors is crucial to enhance the reliability of SCADA systems. In this context, the utilization of quantum key distribution (QKD) key pairs is proposed to uphold integrity in smart grid communications. This paper presents a fibre optic blockchain network designed to manage and utilize cryptographic keys, facilitating the authentication of peer-to-peer (P2P) communications in SCADA systems. This demonstration underscores the feasibility of employing QKD and blockchain to further strengthen the integrity and authentication of smart grid communications. Additionally, this paper delves into discussing the performance metrics and overhead expenses of the proposed scheme in comparison with existing state-of-the-art proposals. Simulation results highlight the significant impact of blockchain size on the system setup’s throughput and latency.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".