A Smart Digital Twin Enabled Security Framework for Vehicle-to-Grid Cyber-Physical Systems
Bibliographic record
Abstract
The rapid growth of electric vehicle (EV) penetration has led to more flexible and reliable vehicle-to-grid-enabled cyber-physical systems (V2G-CPSs). However, the increasing system complexity also makes them more vulnerable to cyber-physical threats. Coordinated cyber attacks (CCAs) have emerged as a major concern, requiring effective detection and mitigation strategies within V2G-CPSs. Digital twin (DT) technologies have shown promise in mitigating system complexity and providing diverse functionalities for complex tasks such as system monitoring, analysis, and optimal control. This paper presents a resilient and secure framework for CCA detection and mitigation in V2G-CPSs, leveraging a smart DT-enabled approach. The framework introduces a smarter DT orchestrator that utilizes long short-term memory (LSTM) based actor-critic deep reinforcement learning (LSTM-DRL) in the DT virtual replica. The LSTM algorithm estimates the system states, which are then used by the DRL network to detect CCAs and take appropriate actions to minimize their impact. To validate the effectiveness and practicality of the proposed smart DT framework, case studies are conducted on an IEEE 30 bus system-based V2G-CPS, considering different CCA types such as malicious V2G node or control command attacks. The results demonstrate that the framework is capable of accurately estimating system states, detecting various CCAs, and mitigating the impact of attacks within 5 seconds.
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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 | 0.001 |
| 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 | 0.001 |
| 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".