Resilient Cyber-Attack Detection and Mitigation in Grid-Tied PEC9 Inverter Using A3C-Based Adaptive Control
Bibliographic record
Abstract
In this paper, the cyber security problem of multi-level inverters (MLIs) in the grid-connected mode is addressed. The cyber-attack can falsify the measurement signals that are transmitted during insecure communication networks. In addition to targeting the data integrity., the cyber-attack can compromise the data availability transmitted during insecure communication networks. For this purpose., a two-stage resilient scheme is developed for identification and mitigation of denial-of-service (DoS) attacks in the measurement signals. i) a detection mechanism is designed by a convolutional neural network observer (CNNO) to estimate the signal transmitted during the insecure communication network., compare the estimated signal with the observed signal., and identify DoS theaters in the system., and ii) the mitigation is realized by an adaptive controller. In the mitigation mechanism., the asynchronous advantage actor-critic (A3C) is utilized to adaptively adjust coefficients embedded in the control structure. By interacting with the A3C learning with the multi-level inverter., the destructive effects of DoS threats are eliminated. The performance of the proposed two-stage resilient technique is evaluated on the MLI compromised by DoS attacks. The simulation results reveal that despite the cyber-attacks being able to impose a high level of instability on the multi-level inverter., the proposed scheme can ensure the ideal operation system from the stability and security point of view.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".