Resilience Framework for Power Electronic Systems Against Cyber-Physical Attacks: A Review
Bibliographic record
Abstract
This paper presents a systematic overview of the resilience framework for power electronics converter-based cyber-physical systems (CPSs), emphasizing end-to-end responses to the emerging challenges posed by cyber-physical attacks. While recent advancements in control and communication have enhanced the functionalities of power electronic converters in applications such as electric vehicles and smart grids, such advancements have also introduced new vulnerabilities, particularly to cyber-physical attacks. The existing literature tends to focus on isolated research areas, and a comprehensive review that encompasses strategies applicable before, during, and after an attack remains lacking. To address this gap, this paper categorizes state-of-the-art research into four key stages of an attack: anticipate and prepare, resist and absorb, detect and evaluate, and recover. Topics reviewed in this paper include converter and cyber-physical attack modeling, converter-based CPS testbeds, system hardening at the cyber and physical layers, attack detection, post-attack evaluation, and recovery strategies. Real-world case studies and practical regulations are also analyzed. Additionally, challenges and opportunities of the resilience framework are discussed. Despite notable advancements in modeling and identifying cyber-physical attacks, considerable efforts are still required to improve attack mitigation strategies and recovery mechanisms.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".