Enhancing WAMS Communication Network Against Delay Attacks
Bibliographic record
Abstract
Smart grid is a typical cyber-physical system, which presents the dependence of power system operations on cyber infrastructure for control, monitoring, and protection purposes. The rapid deployment of synchrophasor measurements units (PMUs) supporting wide area measurement system (WAMS) in the smart grid transmission system has opened opportunities to enhance the grid operations through the introduction of WAMS applications. However, the increased deployment of synchrophasor technologies increases the effective attack surface available to attackers and exposes WAMS applications. Such applications have strict and stringent delay requirements, e.g., end-to-end delay as well as delay variation between measurements from different PMUs. In this paper, we present a mathematical model for constructing forwarding trees for PMUs' measurements which satisfy the end-to-end delay as well as the delay variation requirements of WAMS applications at data concentrators. We illustrate that simple shortest path routing will result in larger fraction of data drop and that our method will achieve better delivery rate. We also validate our method against delay attacks using real-time co-simulation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".