Cybersecurity in maritime power systems: A comprehensive review of cyber threats and mitigation techniques
Bibliographic record
Abstract
Integration of communication networks and Shipboard Microgrids (SHMGs) brings significant benefits in the advanced control, monitoring, and remote diagnostics, particularly facilitating the data exchange between various generation components, such as sustainable energy resources, energy storage systems, and connected loads. However, the utilization of communication technologies brings serious cyber-security challenges that highly threaten maritime power systems from a security and stability perspective. This paper aims to present a comprehensive review of cybersecurity issues in marine power systems. Vulnerable points of the system to cyber-attacks, such as phasor measurement units and area control error channels are elaborated. Prevalent attacks in the load frequency control, such as denial of service and false data injection, covert attacks, and reply attacks are discussed. Recent detection/mitigation mechanisms in the cybersecurity field to tackle various cyber-attacks have been introduced. In the detection part, various observers, such as the Kalman filter, Luenberger observer, and machine learning algorithms are studied. In the mitigation mechanism, various methodologies, such as active disturbance rejection control and model predictive control are presented. This survey reviews the recent cyber-security developments and challenges in SHMG, and it is helpful for contemporary researchers in the field of cybersecurity in maritime power systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".