Data analytics for cybersecurity enhancement of transformer protection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Electric power substations are experiencing an accelerated pace of digital transformation including the deployment of LAN-based IEC 61850 communication protocols that facilitate accessibility to substation data while also increasing remote access points and exposure to complex cyberattacks. In this environment, machine learning algorithms will play a vital role in cyberattack detection and mitigation and natural questions arise as to the most effective models in the context of smart grid substations. This paper compares the performance of three autoencoder-based anomaly detection systems including linear, fully connected, and convolutional autoencoders, as well as long short-term memory (LSTM) neural network for cybersecurity enhancement of transformer protection. The simulation results indicated that the LSTM model outperforms the other models for detecting cyberattacks targeting asymmetrical fault data. The linear autoencoder, fully connected autoencoder and 1D CNN further outperform the LSTM model for detecting cyberattacks targeting the symmetrical fault data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it