Latency-aware segmentation and trust system placement in smart grid SCADA networks
Bibliographic record
Abstract
This paper proposes a latency-aware trust system placement scheme for smart grid SCADA networks. Trust systems are specialized security devices that are deployed to provide cyber protection to supervisory control and data acquisition (SCADA) systems. Their functionalities include firewalling and intrusion detection. They are capable of monitoring both types of traffic, ingress and egress. Only a selected number of nodes are equipped with trust systems due to budgetary constraints. Those nodes are known as the trust nodes. As trust nodes are responsible for distributing time critical messages, it is important to consider the impact of latency in the selection of trust nodes. Network segmentation is a commonly used way of trust node computations. This paper proposes a latency-aware segmentation approach that exploits the graph theoretic properties of minimum spanning trees (MSTs). Numerical results are obtained through case studies for the IEEE BUS 118 test system topology. The results reveal that the proposed scheme is capable of reducing the impact of latency.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".