Neural Fictitious-Self Play-Based Cyber-Layer Defense for Frequency Control in Microgrids Against FDI Attacks
Bibliographic record
Abstract
Securing secondary frequency control against increasing false data injection (FDI) attacks is crucial in microgrid systems. Although various detection systems (DSs) have been proposed for microgrids, false positives (FPs) and false negatives (FNs) in DSs introduce imperfect observations to the cyber defense system. Improper defense actions may reduce the system performance due to additional time delay and/or resource utilization. This paper designs a decentralized optimal decision-making scheme for cyber-layer defense to secure microgrid secondary frequency control against rational FDI attacks. Besides the capability of tackling imperfect observations from DSs, the proposed optimal defense decision-making scheme can maximize the long-term reward rather than a one-shot reward in response to FDI attacks. A multi-stage security game model is formulated, and cyber-physical states and controllability Gramians are jointly considered in the payoff function. The strategy realization-equivalent rule and Nash equilibrium (NE) are introduced to derive the optimal defense policy. A neural fictitious self-play (NFSP) is introduced to learn the optimal defense strategy. Simulation results show that the proposed method increases the successful defense ratio by 21.29% compared with the stochastic game solution when imperfect observations of DSs are considered.
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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) | 0.001 | 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) | 0.002 | 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".