Detection of Cyber Attacks on Synchro-Phasor Network Targeting Topology Detection Application of Power Distribution Grids
Bibliographic record
Abstract
Inferring the accurate topology of Power Distribution Networks (PDNs) is necessary for the operation of their major applications, e.g., state estimation and voltage control. The state-of-the-art Topology Detection (TD) methods benefit from Deep Learning (DL) models as well as high-resolution and synchronized data from Micro Phasor Measurement Units ($\mu$ PMUs) to accurately identify the PDN topology in real-time. Such deployment of TD methods, however, makes them prone to cyber threats due to vulnerabilities of $\mu$ PMUs communication network. On this basis, this paper analyzes the performance of the aforementioned TD methods in the presence of cyber attacks against synchro-phasor networks of PDNs. It demonstrates how a well-crafted cyber attack can mislead the TD method and portray a fake topology to the operator’s control applications. To do so, first, a threat model based on the vulnerabilities of synchro-phasor networks is proposed. Second, a DL-based TD method is adopted and trained for different topologies and loading conditions of a PDN based on the data received from the synchro-phasor network. Third, a cyber attack model that compromises the $\mu$ PMUs data is developed to falsify the detected topology of the PDN. Fourth, the impact of falsified topology on the operation of the voltage control application is demonstrated. Finally, a detection method is proposed to identify the developed cyber attack. The attack model and the detection method are evaluated using the IEEE 33-bus benchmark.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".