A Flexible and Robust Framework for Intrusion Detection in DER Cyber-Physical Security
Bibliographic record
Abstract
The rapid integration of distributed energy resources (DERs) into modern power systems has revolutionized energy generation and distribution, giving rise to a highly dynamic and interconnected cyber-physical ecosystem. However, this evolution has also expanded the attack surface, making DERs increasingly vulnerable to sophisticated cyber-physical threats. Traditional intrusion detection systems (IDSs) primarily focus on algorithmic performance, often neglecting the architectural flexibility required to address the heterogeneity and flexibility demands of DER environments. This work proposes a flexible and robust intrusion detection framework specifically tailored for DER cyber-physical security. The framework integrates both cyber and physical data streams, enabling comprehensive anomaly detection and enhanced situational awareness. It adopts a defense-in-depth strategy to strengthen resilience and employs a microservices-based design using open-source tools to ensure flexibility and ease of integration with existing energy infrastructure. The proposed framework is deployed on a Raspberry Pi and validated in a microgrid lab environment. Evaluation results demonstrate its effectiveness in identifying diverse cyber-physical attack scenarios, while highlighting the benefits of architectural adaptability and operational robustness.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".