Detection of Cyber-Physical Attacks Using Optimal Recursive Least Square in an Islanded Microgrid
Bibliographic record
Abstract
Islanded microgrids (IMGs) are defined as low-inertia systems compared to conventional power grids due to existing inverter-based topologies and lack of heavy rotational masses in their structures. In this regard, IMGs require an accurate load frequency control (LFC) scheme to regulate the frequency response through a cyber layer on top of the physical layer. This multi-layer structure and the sensitivity of LFC schemes to any disturbance, however, makes MGs an appealing target for a variety of cyber-physical attacks (CPAs). This paper introduces an online detection algorithm for CPAs in IMGs by the use of a recursive least square method along with forgetting factor (RLS-FF). The simulation results verify the performance of the developed detection schemes, particularly when the RLS-FF approach coefficients, i.e., covariance matrix and forgetting factor are optimally selected using particle swarm optimization (PSO) algorithm.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".