Optimal Trust System Placement in Smart Grid SCADA Networks
Bibliographic record
Abstract
The objective of this paper is to propose a trust system placement scheme for smart grid supervisory control and data acquisition (SCADA) networks. The functionalities of a trust system include firewalling and network intrusion detection. It is capable of monitoring both ingress traffic and egress traffic. In order to minimize the capital expenditure (CAPEX) and the operational expenditure (OPEX), only a selected number of nodes are equipped with trust systems. Those nodes are known as the trust nodes. This paper studies the trust system placement problem from a network topological perspective. It develops a scheme that aims to defend SCADA networks, deploying minimal number of trust nodes. It uses a network segmentation approach to distribute the trust nodes. It considers the minimum spanning tree (MST) as a measure of geographic dispersion. In the segmentation approach, size balancing and geographic dispersion are two main concerns. The segment sizes affect the number of required trust nodes. On the other hand, geographic dispersion affects the response time. The proposed scheme computes trust nodes using linear programming problem (LPP) formulations and local search. Numerical analysis is conducted through case studies for the IEEE test system topologies. It reveals the consistency of performance, better quality of protection, and low computational time. The proposed scheme can be a useful cyber security planning tool for smart grid operators.
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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".