Tensor-based Cybersecurity Analysis of Smart Grids Using IT/OT Convergence
Bibliographic record
Abstract
The expansion of cyberthreat landscape has been driving power utilities to investigate innovative methods for attack detection while leveraging the converged data generated across the grid Information Technology (IT) and Operational Technology (OT) systems. In this paper, we propose a tensor-based cybersecurity data analysis method and we prove its efficiency using tensors of IT and OT data obtained through the cosimulation of an electricity distribution system using wireless Long-Term Evolution (LTE) technology for synchrophasor communications. An approximate CANDECOMP/PARAFAC (CP) decomposition and Higher Order Singular Value Decomposition (HOSVD) are used to exploit the underlying hidden patterns in the low-rank data tensors. The effectiveness of the low-rank modeling using both decompositions is confirmed by demonstrating relatively low reconstruction error. A residual extraction method is also considered to distinguish the normal subspace of tensor dataset from the anomalous dataset resulting from the attacker actions. Finally, we highlight the intrusion detection performance of the proposed method compared to that of the Tensor Robust Principal Component Analysis (TRPCA) and the discrete-time nonlinear autoregressive neural network (NARX).
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".