Accurate and Energy-Efficient Detection of Cyberattacks Against Non-Linear AGC Systems
Bibliographic record
Abstract
Automatic Generation Control (AGC) systems are widely used in interconnected power systems. However, AGC systems fully rely on communicated measurements, making them susceptible to cyber threats, particularly False Data Injection Attacks (FDIAs), which can destabilize the grid. This paper proposes a novel Spiking Neural Networks (SNNs)-based deep learning (DL) framework for detecting FDIAs against AGC systems considering their nonlinearities. The proposed framework combines FDIA-detection accuracy and energy-consumption efficiency by integrating Convolutional Neural Networks (CNNs) for feature extraction, Long Short-Term Memory (LSTM) networks for temporal modeling, attention mechanisms for adaptive focus, and SNNs for energy-efficient computation. Three different variations of the proposed framework—an ANN-based DL model, a fully-spiking model, and a hybrid spiking-ANN-based model—are implemented and analyzed under a high-fidelity set of diverse FDIA scenarios against an AGC system with nonlinearities. Results demonstrate that the proposed Hybrid-SNN DL model can accurately detect FDIAs on AGC systems while reducing DL model’s energy consumption by up to 56%. These findings highlight the potential of SNNs for enhancing cybersecurity in smart grids, offering a sustainable approach to reducing operational costs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".