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 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 | 0.002 |
| 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".