Optimization of Trust Node Assignment for Securing Routes in Smart Grid SCADA Networks
Bibliographic record
Abstract
The move toward a smart power grid has widened the range of cyber vulnerabilities in supervisory control and data acquisition (SCADA) systems. Specialized security hardening devices, such as the trust systems, are being developed to protect energy SCADA networks from possible cyberattacks. The trust systems are network security resources that monitor and act on malicious packets. A node is said to be a trust node when it is equipped with a trust system. This paper investigates the optimal security deployment problem in resource-constrained SCADA networks. It proposes two deployment schemes for inline security devices: 1) link coverage maximization; and 2) minimal path tolerance (MPT). The first scheme focuses on the overall monitoring coverage. It is formulated as a quadratic assignment problem. The second scheme focuses on the hop distance between consecutive trust nodes. It uses a heuristic approach that deploys trust nodes in a distributive manner. The proposed schemes are evaluated considering the IEEE test case topologies under various scenarios. Numerical results demonstrate that the proposed schemes are capable of achieving their primary goals. They also reveal a performance tradeoff between the proposed schemes in the highly resource-constrained scenarios where MPT offers a better distributiveness.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".