Time-Synchronization Attack on Data Aggregation in Wide-Area Damping Controllers
Bibliographic record
Abstract
The advent of wide-area measurement systems (WAMSs) in modern power systems enables deployment of wide-area damping controllers (WADCs) to effectively deal with dominant oscillation modes. However, the reliance of WAMSs on information and communication technologies (ICTs) exposes WADCs to potential cyber attacks. To come up with effective countermeasures, extensive knowledge about the existing vulnerabilities and possible cyber attacks is required. On this basis, this paper presents an attack model against WADCs by exploiting the vulnerabilities in the time-alignment strategies utilized by phasor data concentrators (PDCs) to aggregate the stream of phasor measurements. In this attack, the adversary manipulates data timestamp of phasor measurement units (PMUs) to compromise PDCs functionality, causing dropout in the WADC's critical phasor measurements data. To obtain the targeted PMUs and manipulated timestamp, a stochastic mixed-integer linear (MIL) model is developed from adversary prospective considering uncertainty of communication delay. The time-domain dynamic study on two-area Kundur test system demonstrates that the developed attack can jeopardize the WADC performance and even cause system instability.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".