An SDN-Based Framework for Cyber-Physically Coordinated Voltage Support of Virtual Power Plants Against DoS Attacks
Bibliographic record
Abstract
The integration of distributed energy resources (DERs) into smart grids is accelerating worldwide. However, the growing reliance on information and communication technologies (ICTs) for real-time monitoring and control in large-scale DER aggregations introduces significant cybersecurity risks. Among these, denial-of-service (DoS) attacks pose a critical threat, with the potential to severely disrupt system operations. To address these challenges, this paper proposes a security-by-design framework for large-scale DER aggregation in the context of virtual power plants (VPPs), with a focus on enhancing the resilience of voltage support services. Central to this framework is a cyber-physical coordinated control architecture that leverages software-defined networking (SDN) to dynamically adapt to variations in both the cyber and physical layers. Specifically, a cyber-aware voltage control algorithm is developed to coordinate DERs based on the real-time status of the communication network, while a physically informed network configuration algorithm adjusts communication paths according to voltage support priorities. The proposed approach is evaluated through cyber–physical co-simulations on both the IEEE 123-bus test system and the 240-bus test system, based on a real-world distribution system located in the Midwest U.S., under server DoS attack scenarios. Results demonstrate that the SDN-enabled coordination significantly mitigates server DoS attacks, confirming the framework’s effectiveness and robustness. This work offers a scalable and resilient solution for securing large-scale DER aggregation and integration against evolving cyber threats.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".