Unsupervised Stacked Autoencoders for Anomaly Detection on Smart Cyber-physical Grids
Bibliographic record
Abstract
Smart Cyber Physical Grids are the new wave of power system technology that integrates networks of sensors with power stations for more efficient power generation and distribution. While utilizing communication networks is accompanied with tremendous advantages, it also increases the vulnerability of power systems to cyber attacks. Many methods for security and attack detection have been proposed in literature; however, most papers do not consider the imbalance of data in real power systems. In this paper, we propose a deep learning based method, referred to as Ensemble Stacked AutoEncoder (ESAE), aimed at tackling the problem of data imbalance. This method achieves superior performance on imbalanced data by developing a deep representation learning model to construct new balanced representations. The detection accuracy and model performance is improved by utilizing an ensemble architecture based on Stacked Autoencoders and Random Forest classifiers to detect attacks from the new representations. The proposed method is tested on all degrees of data imbalance using test cases of IEEE 14-bus, 30-bus, and 57-bus systems. Comparisons are made to several classifiers to demonstrate the effectiveness of the proposed 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 | 0.000 |
| 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".