RTAP: A Real-Time Model for Attack Detection and Prediction in Smart Grid Systems
Bibliographic record
Abstract
One main challenge of smart grid systems is the cascading failures caused by cyber-attacks, which can affect the power and communication networks. Many testbeds have been proposed to model the impact of cyber-attacks on these two networks; however, many lack a realistic propagation model or simulate real-time behavior. In this paper, we develop a novel real-time testbed that models both the power and communication networks to analyze cyber-attacks’ impacts on smart grid systems. Our proposed testbed can model various cyber-attacks on both networks and analyze the propagation of failure within the system. To create a realistic model of smart grid systems, we utilize real-time simulators and implement a failure propagation model. Using this testbed, we propose a prediction model to detect and predict failures after cyber-attacks have impacted the system. This model can detect cyber-attacks in the early stages of failure propagation and predict the state of each power and communication component following the propagation. We prove this model is realistic using the failure propagation factor and validate its effectiveness by employing an IEEE 14-bus test case, showcasing its high accuracy in detecting various types of attacks.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".