Cyber-Physical Attacks Targeting Communication-Assisted Protection Schemes
Bibliographic record
Abstract
The dependence of modern societies on electric energy is ever increasing by the emergence of smart cities and electric vehicles. This is while unprecedented number of cyber-physical hazards are threatening the integrity and availability of the power grid on a daily basis. On one hand, physical integrity of power systems is under threat by more frequent natural disasters and intentional attacks. On the other hand, the cyber vulnerability of power grids is on the rise by the emergence of smart grid technologies. This underlines an imminent need for the modeling and examination of power grid vulnerabilities to cyber-physical attacks. This paper examines the vulnerability of the communication-assisted protection schemes like permissive overreaching transfer trip to cyberattacks using a co-simulation platform. The simulation results show that the transient angle stability of power systems can be jeopardized by cyberattacks on the communication-assisted protection schemes. To address this vulnerability, two physical solutions including the deployment of communication channel redundancy, and a more advanced communicated-assisted protection scheme, i.e., directional comparison unblocking scheme (DCUB), are considered and tested. The proposed solutions address the vulnerability of the communication-assisted protection schemes to distributed denial of service attack to some extent. Yet, the simulation results show the vulnerability of the proposed solutions to sophisticated cyberattacks like false data injection attacks. This highlights the need for the development of cyber-based solutions for communication channel monitoring.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".