A Dynamic Secret-Based Encryption Scheme for Smart Grid Wireless Communication
Bibliographic record
Abstract
Integrating information network into power system is the key for realizing the vision of smart grid, but also introduces many security problems. Wireless communication offers the benefits of low cost, rapid deployment, shared communication medium, and mobility; at the same time, it causes many security and privacy challenges. In this paper, the concept of dynamic secret is applied to design an encryption scheme for smart grid wireless communication. Between two parties of communication, the previous packets are coded as retransmission sequence, where retransmitted packet is marked as “1” and the other is marked as “0.” During the communication, the retransmission sequence is generated at both sides to update the dynamic encryption key. Any missing or misjudging in retransmission sequence would prevent the adversary from achieving the keys. In our experiments, a smart grid platform is built, employing the ZigBee protocol for wireless communication. And a dynamic secret-based encryption demo system is designed based on this platform. The experiment results show that the retransmission and packet loss in ZigBee communication are inevitable and unpredictable, and it is impossible for the adversary to track the updating of the dynamic encryption key.
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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 | 0.000 | 0.001 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".