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使用SQUARE方法制定自主船舶的网络安全要求。

Formulating Cybersecurity Requirements for Autonomous Ships Using the SQUARE Methodology.

作者信息

Yoo Jiwoon, Jo Yonghyun

机构信息

DSLAB Company Ltd., Seoul 08511, Republic of Korea.

出版信息

Sensors (Basel). 2023 May 24;23(11):5033. doi: 10.3390/s23115033.

DOI:10.3390/s23115033
PMID:37299766
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10255142/
Abstract

Artificial intelligence (AI) technology is crucial for developing autonomous ships in the maritime industry. Autonomous ships, based on the collected information, recognize the environment without any human intervention and operate themselves using their own judgment. However, ship-to-land connectivity increased, owing to the real-time monitoring and remote control (for unexpected circumstances) from land; this poses a potential cyberthreat to various data collected inside and outside the ships and to the applied AI technology. For the safety of autonomous ships, cybersecurity around AI technology needs to be considered, in addition to the cybersecurity of the ship systems. By identifying various vulnerabilities and via research cases of the ship systems and AI technologies, this study presents possible cyberattack scenarios on the AI technologies applied to autonomous ships. Based on these attack scenarios, cyberthreats and cybersecurity requirements are formulated for autonomous ships by employing the security quality requirements engineering (SQUARE) methodology.

摘要

人工智能（AI）技术对于海事行业开发自主船舶至关重要。自主船舶基于收集到的信息，无需任何人为干预即可识别环境，并利用自身判断进行自主运行。然而，由于来自陆地的实时监测和（针对意外情况的）远程控制，船岸连通性增强；这对船舶内外收集的各种数据以及所应用的人工智能技术构成了潜在的网络威胁。为了自主船舶的安全，除了船舶系统的网络安全外，还需要考虑围绕人工智能技术的网络安全。通过识别各种漏洞以及船舶系统和人工智能技术的研究案例，本研究提出了适用于自主船舶的人工智能技术可能遭受的网络攻击场景。基于这些攻击场景，采用安全质量要求工程（SQUARE）方法为自主船舶制定了网络威胁和网络安全要求。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d045/10255142/cf9257c9f77f/sensors-23-05033-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d045/10255142/78e489335faa/sensors-23-05033-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d045/10255142/cf9257c9f77f/sensors-23-05033-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d045/10255142/d2f596c9e813/sensors-23-05033-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d045/10255142/1aec4c4e872c/sensors-23-05033-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d045/10255142/0d626352ad65/sensors-23-05033-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d045/10255142/0fcde663fac6/sensors-23-05033-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d045/10255142/bbf347471445/sensors-23-05033-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d045/10255142/78e489335faa/sensors-23-05033-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d045/10255142/cf9257c9f77f/sensors-23-05033-g012.jpg

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本文引用的文献

1
Path Tracking Control of Autonomous Vehicles Subject to Deception Attacks via a Learning-Based Event-Triggered Mechanism.
IEEE Trans Neural Netw Learn Syst. 2021 Dec;32(12):5644-5653. doi: 10.1109/TNNLS.2021.3056764. Epub 2021 Nov 30.
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An Autonomous Path Planning Model for Unmanned Ships Based on Deep Reinforcement Learning.基于深度强化学习的无人船自主路径规划模型。
Sensors (Basel). 2020 Jan 11;20(2):426. doi: 10.3390/s20020426.