A Generalizable Deep Neural Network Method for Detecting Attacks in Industrial Cyber-Physical Systems
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
Today's power systems utilize smart technology to improve the efficacy of power distribution. Using cyber-physical components in the power system such as smart grids can introduce vulnerabilities such as false data injection (FDI) that can cost millions. Deep learning (DL) is an emerging technology that mimics the human brain to process complex problems. In DL, relevant features are extracted automatically to make a meaningful decision out of the data. This article proposes an attack detection method that utilizes DL techniques for detecting FDI attacks. The proposed methodology assumed the problem of varying sparsity attacks in the system, in which attacks can occur at any subset of measurements, as well as the problem of imbalanced training data in real systems. Thus, a deep neural network with regularization techniques including dropout layers and adaptive optimization is proposed for superior generalization to varying sparsity in FDI attacks. An experimental environment is established on simulated power systems of varied sizes and compared with alternative state-of-the-art schemes. The proposed scheme outperforms all of them including robustness to data imbalance and also, it takes lesser time than neural networks of the similar architecture.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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