Quantum Computing Threats to IEC 62351 Cryptographic Algorithms: An In-Depth Analysis
Bibliographic record
Abstract
Quantum computing, power systems, and communication represent three distinct fields that converge to address the complexities involved in the quantum cybersecurity of power systems. Quantum computers present a serious threat to cybersecurity by potentially affecting traditional encryption methods, which could compromise sensitive data and disrupt the integrity of secure communications. As communication and data transformation serve as the backbone of modern power grid monitoring, control, and protection systems, the growing awareness of cyber-vulnerabilities in power grids has made cybersecurity a pressing concern. The IEC 62351 standard family has been developed to provide security recommendations for various power system communication protocols and is emerging as a leading cybersecurity standard for power systems. However, the vulnerabilities of these standards to quantum threats have yet to be fully explored. This article conducts a comprehensive analysis of security threats and vulnerabilities in modern power systems against quantum attacks. It systematically reviews the security considerations outlined in IEC 62351 for safeguarding various aspects, such as IEC 61850 messages, key management, XML file security, and TCP/IP profiles. The analysis reveals that almost all encryption algorithms mandated and recommended by IEC 62351 are vulnerable to quantum attacks such as Grover's and Shor's algorithms.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".