Adapting the Pretty Good Privacy Security Style to Power System Distributed Network Protocol
Bibliographic record
Abstract
Power system modernization with increasing operation automation and integration results in growing computer network access. This facilitates cyber-attackers' capabilities to assume control over power system operations that could cause serious blackouts. Security therefore becomes a critical issue for DNP3, a commonly used protocol for power system communications. This paper proposes cyber-security based on Pretty Good Privacy (PGP) for DNP3 to strengthen computer network security. This PGP-based cyber-security provides authentication capabilities using public key cryptography, with enhanced performance using symmetric keys for most of the encryption. This paper provides a symmetric cipher key exchange mechanism using PGP-based cyber-security to further enhance the power system security. The proposed PGP-based cyber-security is implemented as a pseudo-layer below the DNP3 data-link layer to minimize any impact on the DNP3 specifications and the operations of original DNP3 devices. This PGP-based cyber-security provides confidentiality, identity authentication, transmission content authentication, and nonrepudiation
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".