Game theoretic vulnerability management for secondary frequency control of islanded microgrids against false data injection attacks
Bibliographic record
Abstract
Abstract While most existing works ignore securing the communication of control signals in microgrids' centralized secondary frequency control, here, a stochastic game between the microgrid control centre (MGCC) and the attacker for enhancing the vulnerability of the MGCC to false data injection (FDI) attack (wireless spoof attack) is proposed. The vulnerability to wireless spoof attack is assessed based on the controllability Gramian when the FDI attack is modelled as the malicious control input that aims to drive the microgrid state to undesired values. In the formulated zero‐sum two‐player Markov game, the state is uniquely associated with the vulnerability index defined by the trace of the controllability Gramian with respect to the attack input. Moreover, the utility function of the stochastic game includes not only the costs related to conducting spoof attack and encryption actions but also the possible remedy costs associated with the resulted vulnerability levels. In turn, the potential impacts of the cyber‐layer action choices on the performance of the physical power system are considered when designing the optimal attack and defence strategies. A distribution feeder system with four distributed generators (DGs) is used for simulation studies. The vulnerability assessment results show that the vulnerability level increases when the attacker compromises more on the communication links between the MGCC and DGs. In the simulated game, mixed stationary attack and defence strategies are predominate when considering the uncertainty of the other player.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".