ORCID
- K Tam: 0000-0003-2840-5715
Abstract
Contemporary maritime operations such as shipping are a vital component constituting global trade and defence. The evolutiontowards maritime autonomous systems, often providing significant benefits (e.g., cost, physical safety), requires the utilisation ofartificial intelligence (AI) to automate the function of a conventional crew. However, unsecured AI systems can be plagued withvulnerabilities naturally inherent within complex AI models. The adversarial AI threat, primarily only evaluated in a laboratoryenvironment, increases the likelihood of strategic adversarial exploitation and attacks on mission-critical AI, including maritimeautonomous systems. This work evaluates AI threats to maritime autonomous systems in situ. The results show that multipleattacks can be used against real-world maritime autonomous systems with a range of lethality. However, the effects of AI attacksvary in a dynamic and complex environment from that proposed in lower entropy laboratory environments. We propose a set ofadversarial test examples and demonstrate their use specifically in the marine environment. The results of this paper highlightsecurity risks and deliver a set of principles to mitigate threats to AI, throughout the AI lifecycle, in an evolving threat landscape.
DOI Link
Publication Date
2023-06-01
Publication Title
AI, Computer Science and Robotics Technology
Acceptance Date
2023-03-22
Deposit Date
2023-03-23
Embargo Period
2023-04-28
Funding
This work was supported by the Turing’s Defence and Security programme through a partnership with the UK government in accordance with the framework agreement between GCHQ & The Alan Turing Institute. The authors would also like to thank the University of Plymouth for their use of their autonomous fleet in order to collect real world data.
Keywords
maritime cyber security, adversarial AI, maritime autonomous systems
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Walter, M., Barrett, A., Walker, D., & Tam, K. (2023) 'Adversarial AI Testcases for Maritime Autonomous Systems', AI, Computer Science and Robotics Technology, . Available at: 10.5772/ACRT.15
