A study of resource-constrained cyber security planning for smart grid networks
Bibliographic record
Abstract
This paper studies cyber security planning issues in resource-constrained smart grid networks. In particular, it proposes a centrality-based trust system placement scheme for energy SCADA systems. It aims to utilize centrality measurements to improve cyber protection in resource-constrained scenarios. The role of centrality measurements is to rank nodes based on their importance in a network. Trust systems are specialized security devices that are capable of firewalling and network intrusion detection. They monitor both types of traffic, ingress and egress. They are mainly deployed to provide cyber protection to supervisory control and data acquisition (SCADA) systems. Due to budgetary constraints, only a selected number of nodes are equipped with trust systems. Those nodes are known as the trust nodes. The proposed scheme uses linear programming problem (LPP) formulations to select the trust nodes. Numerical results are obtained through case studies for the IEEE BUS 30 and BUS 57 test system topologies. The results reveal that the proposed scheme is capable of improving quality of cyber protection in resource-constrained scenarios.
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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".