Bridging the Gap: Enhancing Maritime Vessel Cyber Resilience through Security Operation Centers
Bibliographic record
Abstract
Increasingly disruptive cyber-attacks in the maritime domain have led to more efforts being focused on enhancing cyber resilience. From a regulatory perspective, there is a requirement that maritime stakeholders implement measures that would enable the timely detection of cyber events, leading to the adoption of Maritime Security Operation Centers (M-SOCs). At the same time, Remote Operation Centers (ROCs) are also being discussed to enable increased adoption of highly automated and autonomous technologies, which could further impact the attack surface of vessels. The main objective of this research was therefore to better understand both enabling factors and challenges impacting the effectiveness of M-SOC operations. Semi-structured interviews were conducted with nine M-SOC experts. Informed by grounded theory, incident management emerged as the core category. By focusing on the factors that make M-SOC operations a unique undertaking, the main contribution of this study is that it highlights how maritime connectivity challenges and domain knowledge impact the M-SOC incident management process. Additionally, we have related the findings to a future where M-SOC and ROC operations could be converged.
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 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".