Cyber risk communication during vessel incident management: A case study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The maritime cyber risk management guidelines developed by the International Maritime Organisation (IMO) highlight communication as a key aspect of the risk management process. This research sought to build upon previous studies highlighting incident communication as a critical part of the ship-to-SOC cyber incident management process. This research adopted a single case study-mixed methods design (CS-MM) featuring a primary case study that includes a nested mixed methods approach. The site for the case study was an M-SOC. The first phase of the case study involved interviews with 5 M-SOC personnel. For the second phase, an exploratory sequential design was applied. The quantitative data collection involved a survey with 10 vessel Information Technology (IT) and Operational Technology (OT) professionals, with 3 follow-up interviews conducted for the qualitative data collection stage. Our findings highlighted how a cyber incident dashboard and alert report complement each other in creating a shared recognised cyber picture (sRCP) between all the vessel incident management stakeholders. The sRCP, therefore, becomes the actionable element of the communication. The case study also sheds light on practical design considerations for enhancing the cyber situation awareness (CSA) of vessel cyber incident dashboards. Specifically, survey results revealed that highlighting the cyber risk of non-response to a security warning was the highest-ranked contextual information. Additionally, detection of potentially suspicious activity emerged as the risk finding that vessel IT teams highlighted as having the highest notification priority. Finally, the top alert grouping approaches were by warning type and by priority.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it