The survivability of autonomous vessels from cyber-attacks

This literature review is an investigation into the survivability of an automated vessel. More specifically, this study investigates an automated vessel’s susceptibility, vulnerability and ability to recover from a cyber-security-related threat. The future of maritime shipping is trending towards transitioning to automated vessels. Automated vessels have the potential to provide significant financial and logistical benefits for shipping companies and stakeholders. The study aims to evaluate the current survivability features of an automated vessel. To achieve this aim objectively, a literature review was conducted into potential threats to automated vessels, the security features they have to combat said threats, and their ability to recover from a cyberattack. It was conducted with the defined scope of ‘ship survivability’. Moreover, it was filtered into four areas: susceptibility to a threat, vulnerability to the effects of a threat, the ability to recover from an attack and case studies of previous relevant attacks. The result of the literature evaluation indicates a significant vulnerability of automated vessels. Automated vessels were found to have a high susceptibility to cyber-attacks, the effects of which have potentially significant financial effects, a high chance of significant damage to the vessel, a significant chance of injury or fatality and a low ability to recover from an attack. This study can indicate to the marine transport industry the ‘gaps in the market’ concerning the survivability, susceptibility, vulnerability and ability to recover from an attack against an automated vessel.

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Publication Details

Journal
Journal of Marine Engineering & Technology
Published
2024-12-06
DOI
https://doi.org/10.1080/20464177.2024.2428022
Citations
5
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
1.74
Controls
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article

The survivability of autonomous vessels from cyber-attacks

Eduardo Blanco‐Davis, Edward Shaw, Jin Wang, Steve Symes et al.
5 citations
Journal of Marine Engineering & Technology
Network Security and Intrusion Detection
1.74
article

The survivability of autonomous vessels from cyber-attacks

Eduardo Blanco‐Davis, Edward Shaw, Jin Wang, Steve Symes, Tony Graham
article en
5 citations

Abstract

This literature review is an investigation into the survivability of an automated vessel. More specifically, this study investigates an automated vessel’s susceptibility, vulnerability and ability to recover from a cyber-security-related threat. The future of maritime shipping is trending towards transitioning to automated vessels. Automated vessels have the potential to provide significant financial and logistical benefits for shipping companies and stakeholders. The study aims to evaluate the current survivability features of an automated vessel. To achieve this aim objectively, a literature review was conducted into potential threats to automated vessels, the security features they have to combat said threats, and their ability to recover from a cyberattack. It was conducted with the defined scope of ‘ship survivability’. Moreover, it was filtered into four areas: susceptibility to a threat, vulnerability to the effects of a threat, the ability to recover from an attack and case studies of previous relevant attacks. The result of the literature evaluation indicates a significant vulnerability of automated vessels. Automated vessels were found to have a high susceptibility to cyber-attacks, the effects of which have potentially significant financial effects, a high chance of significant damage to the vessel, a significant chance of injury or fatality and a low ability to recover from an attack. This study can indicate to the marine transport industry the ‘gaps in the market’ concerning the survivability, susceptibility, vulnerability and ability to recover from an attack against an automated vessel.

Journal of Marine Engineering & TechnologyVol. 24(3)
Liverpool John Moores University (GB)
Openalex Percentile: Top 14%
Network Security and Intrusion Detection
1.74
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