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Enhancing maritime cyber situational awareness: a cybersecurity visualisation for non-experts

Abstract:
Cyber situational awareness is key to mitigating the impacts of cyber threats. However, maritime falls short of its comparative industries, with very little attention given to cyber threats despite the growing concern. In this paper, we explore the use of visualisations as a way to improve the situational awareness of non-experts onboard ships. We designed a visualisation tool with focus on systems that are accessible once onboard. In order to elicit requirements for our visualisations, we conducted semi-structured interviews with experts. We further created a synthetic dataset of attacks that target the systems of ships, which we used to assess the usability of our visualisation. In order to evaluate our visualisations, we conducted a user study with both expert and non-expert users. Our results show that non-expert participants were able to accurately and efficiently detect synthetic attacks targeting ships in an experimental setting, and they were able to use the visualisation to consider what the consequences of these attacks might be. Expert evaluations further suggest the visualisation has merit as a training tool for raising awareness among maritime employees.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.cose.2025.104433

Authors

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
ORCID:
0000-0003-3747-339X
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
ORCID:
0000-0001-7808-0600
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author


Publisher:
Elsevier
Journal:
Computers and Security More from this journal
Volume:
154
Article number:
104433
Publication date:
2025-03-21
Acceptance date:
2025-03-10
DOI:
ISSN:
0167-4048


Language:
English
Keywords:
Pubs id:
2101235
Local pid:
pubs:2101235
Deposit date:
2025-04-14
ARK identifier:

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