Attack Detection and Identification for Automatic Generation Control 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
Integrating today's power systems with communication infrastructure makes them vulnerable to cyber-attacks, which can disrupt their normal operation undetectable. Automatic generation control (AGC) is one of the vulnerable controllers in power grids, since it greatly depends on communication systems. This paper first shows that false data injection attacks (FDIAs) against an AGC system can be carried out stealthily with destructive outcomes. Then, it proposes an anomaly based attack detection and identification method for protecting the AGC system against cyber vulnerabilities. To detect attacks, the proposed method estimates the load frequency control system's states using an unknown input observer (UIO), and calculates the UIO's residual function. A discrepancy between the residual functions and a predefined threshold signifies an FDIA. Different identification UIOs are then used to determine the attack type, i.e., which system parameter(s) is (are) targeted by the attack. The effectiveness of the proposed method is corroborated using simulation results for a three-area power system and the IEEE 39-bus network.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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