Domain Adversarial Transfer Learning for Robust \nCyber-Physical Attack Detection in the Smart Grid
Bibliographic record
Abstract
Thanks to the increasing availability of high-quality data and the success of deep learning algorithms, machine learning (ML)-based classifiers have become increasingly appealing and investigated against sophisticated attacks in complex cyber-physical systems like the smart grid. However, many of these techniques rely on the assumption that the training and testing datasets share the same distribution and class labels in a stationary environment. As such assumption may fail to hold when the system dynamics shift and new threat variants emerge in a non-stationary environment, the capability of trained ML models to adapt in complex operating scenarios will be critical to their deployment in real-world applications. Using cyber-physical attack detection in the smart grid as the targeted application, this research aims to leverage transfer learning-based strategies to improve the robustness of ML classifiers against variations in threat types, locations, and timing in a complex dynamic CPS. \nTo this end, this research investigates and develops domain-adversarial transfer learning schemes for robust intrusion detection against smart grid attacks. \nThe main contributions include: (i) A domain-adversarial transfer learning scheme with customized classifiers for attack detection based on realistic smart grid data collected from a hardware-in-the-loop testbed; (ii) A semi-supervised transfer learning to transfer \nthe knowledge of limited known attack incidences to detect returning threats at a later time with different system dynamics; (iii) A divergence-based transferability analysis and a spatiotemporal domain-adversarial transfer learning scheme for robust detection against spatial and temporal variants. Experiments were conducted on standardized IEEE benchmarks, and the results have demonstrated the promising capability of domain adversarial transfer learning to improve ML robustness against system and attack variations.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".