TADA: A Transferable Domain-Adversarial Training for Smart Grid Intrusion Detection Based on Ensemble Divergence Metrics and Spatiotemporal Features
Bibliographic record
Abstract
For attack detection in the smart grid, transfer learning is a promising solution to tackle data distribution divergence and maintain performance when facing system and attack variations. However, there are still two challenges when introducing transfer learning into intrusion detection: when to apply transfer learning and how to extract effective features during transfer learning. To address these two challenges, this paper proposes a transferability analysis and domain-adversarial training (TADA) framework. The framework first leverages various data distribution divergence metrics to predict the accuracy drop of a trained model and decides whether one should trigger transfer learning to retain performance. Then, a domain-adversarial training model with CNN and LSTM is developed to extract the spatiotemporal domain-invariant features to reduce distribution divergence and improve detection performance. The TADA framework is evaluated in extensive experiments where false data injection (FDI) attacks are injected at different times and locations. Experiments results show that the framework has high accuracy in accuracy drop prediction, with an RMSE lower than 1.79%. Compared to the state-of-the-art models, TADA demonstrates the highest detection accuracy, achieving an average accuracy of 95.58%. Moreover, the robustness of the framework is validated under different attack data percentages, with an average F1-score of 92.02%.
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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 | 0.001 | 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 | 0.001 | 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".