State‐of‐the‐art of cybersecurity in the power system: Simulation, detection, mitigation, and research gaps
Bibliographic record
Abstract
Abstract In a power system, the communication link can be compromised by intruders who can launch cyberattacks by capturing data packets, sending falsified packets, or stopping data packets from reaching their destination. Moreover, intruders can compromise control devices using supply chain attacks, firmware patching attacks, and insider attackers. Numerous cyberattacks have been reported previously, and cyberattacks are becoming more frequent since attackers are aware of their socioeconomic impacts. Extensive research has been conducted on developing platforms to simulate cyberattacks, studying different types of cyberattacks, investigating the adverse effects of a successful cyberattack on different components of the power system, designing ways to detect anomalies in the power system using electrical measurements, and proposing ways to mitigate the adverse effects of the detected cyberattack. This paper presents a review of state‐of‐the‐art of cybersecurity in the power system, reviewing available simulation tools for studying the cybersecurity of the power system, classifying components of the power system vulnerable to cyberattacks, and summarizing the adverse effects of a successful cyberattack on each component in the power system. Furthermore, different types of cyberattacks and detection and mitigation methods are classified. Research gaps in the cybersecurity of the power system are also discussed.
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.
How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".