Divergence-Based Transferability Analysis for Self-Adaptive Smart Grid Intrusion Detection With Transfer Learning
Bibliographic record
Abstract
Machine learning is a popular approach to security monitoring and intrusion detection in cyber-physical systems (CPS) like the smart grid. However, these highly dynamic CPS operating in open environments can result in significant data distribution divergence, which may require the adaptation of a learned model. While transfer learning has been an effective approach to retain the performance against the divergence, there is still limited work on a more fundamental question that can be called <i>transferability</i>: when should one apply transfer learning? To address this challenge, this paper proposes a divergence-based transferability analysis to decide whether to apply transfer learning and autonomically adapt learning-based intrusion detectors. This work first identifies three metrics used to measure the divergence between data distributions, and then explores the relation between detector’s accuracy drop and divergence in extensive temporal, spatial, and spatiotemporal experiments. Two regression models are trained to approximate the divergence-accuracy relation and then used to predict an accuracy drop which determines whether to apply transfer learning. Finally, a state-of-the-art domain adversarial neural network (DANN) classifier is adopted as the transfer learning model. Datasets from real normal operation profiles and simulated attacks are used to validate the effectiveness of the proposed transferability analysis against variations in attack timing, locations, and both. In all three scenarios, the proposed analysis demonstrated high accuracy in predicting accuracy drop from the divergence, with an RMSE lower than 4.20%, and the DANN can be timely triggered to achieve an accuracy improvement over 5.00%.
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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 | 0.001 | 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".