Financially Motivated FDI on SCED in Real-Time Electricity Markets: Attacks and Mitigation
Bibliographic record
Abstract
Given the strong cyber-physical coupling that exists in power systems today and of the future, false data injection (FDI) attacks have been shown to be feasible in tampering measurement devices by exploiting cyber vulnerabilities to mislead state estimation and related applications. For example, a corrupt generator owner, motivated by financial gain, may manipulate meter readings associated with short-term load forecasts and subsequently misguide the decisions of security constrained economic dispatch (SCED) in ex-ante real-time markets. In this paper, we analyze the feasibility of financially motivated FDI attacks in bi-level programming settings where multi-solution uncertainty of SCED is considered. To deter such attacks, a robust incentive-reduction strategy is proposed that can prevent financially motivated FDI attacks for all the possible load distributions and solutions of SCED requiring a minimal number of protected meters. Simulations for the IEEE 14-bus and IEEE 30-bus test systems demonstrate attack feasibility and performance of the proposed mitigation strategy for SCED in real-time markets.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".