Advances in Safety, Security and Cybersecurity of Maritime Autonomous Surface Ships

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 12730

Special Issue Editors


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Guest Editor
Maritime Safety Research Centre, Department of Naval Architecture Ocean and Marine Engineering, University of Strathclyde, Henry Dyer Building, 100 Montrose Street, Glasgow G4 0LZ, UK
Interests: marine systems; safety; sustainability; digital; twins; autonomous ships
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Maritime Safety Research Centre, Department of Naval Architecture Ocean and Marine Engineering, University of Strathclyde, Glasgow G4 0LZ, UK
Interests: autonomous ships; intelligent ship control; intelligent decision-making system; ship maneuvering; automatic ship berthing; collision avoidance systems; maritime industry digitalization

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Guest Editor
Research Group on Safe and Efficient Marine Systems, Marine Technology, Department of Mechanical Engineering, Aalto University, 02150 Espoo, Finland
Interests: autonomous ships; safety; marine systems; cybersecurity; ship propulsion systems

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Guest Editor
Research Group on Safe and Efficient Marine Systems, Marine Technology, Department of Mechanical Engineering, Aalto University, 02150 Espoo, Finland
Interests: safety and systems engineering; risk analysis; maritime safety; winter navigation; autonomous ships
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of autonomous and unmanned ships is expected to respond to the shipping industry and society needs pertinent to the sustainability of shipping operations, resilience of the supply chain, as well as lack of skillful crew in the medium to long term. The recent upsurge of initiatives worldwide toward the development of autonomous and unmanned ships has resulted in an explosion of advances, key enabling technologies, approaches, and tools leading to the building of maritime autonomous surface ships (MASS) prototypes. Next-generation autonomous shipping ecosystems are expected to be of multidisciplinary sociotechnical nature, bearing increasing complexity, while rendering the interactions and interrelations between the involved subsystems/components unpredictable. This introduces new challenges for the design and operation of next-generation MASS, especially pertinent to their safety, security, and cybersecurity, as well as the regulatory and legal frameworks.  

This Special Issue aims to compile the most contemporary concepts, novel and advanced methods, techniques, methodologies, and tools to address the challenges the field of the safety, security, and cybersecurity for maritime autonomous surface ships (MASS). Contributions are expected in the following indicative topics for MASS: safety, cybersecurity, security, regulatory framework, legal jurisdiction, human-machine interactions, training framework, verification and validation, risk, reliability and maintainability, modelling and digital twins, design, and operation.

This Special Issue aims to provide a stimulus for scientists, researchers, and professionals as well as to drive and further develop the current and future research and innovation activities in the areas of safety, security, and cybersecurity for next-generation MASS.

Prof. Dr. Gerasimos Theotokatos
Dr. Yaseen Adnan Ahmed
Dr. Victor Bolbot
Dr. Osiris Valdez Banda
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • maritime autonomous surface ships (MASS)
  • safety, security, and cybersecurity
  • novel methods and tools
  • regulatory framework
  • legal jurisdiction
  • human-machine interactions
  • training framework
  • verification and validation
  • risk
  • reliability and maintainability
  • modeling and digital twins
  • design and operation

Published Papers (8 papers)

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Research

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23 pages, 1710 KiB  
Article
Systematization of Legal Procedures for Collision Avoidance between a Fully Autonomous Ship and a Traditional Manned Ship
by Inchul Kim
J. Mar. Sci. Eng. 2023, 11(10), 1850; https://doi.org/10.3390/jmse11101850 - 22 Sep 2023
Cited by 1 | Viewed by 1481
Abstract
Discussions of autonomous ships are actively being conducted in the industry and by the International Maritime Organization (IMO). In addition, it is anticipated that a significant number of autonomous ships will be operational at sea soon, as a trial run of autonomous ships [...] Read more.
Discussions of autonomous ships are actively being conducted in the industry and by the International Maritime Organization (IMO). In addition, it is anticipated that a significant number of autonomous ships will be operational at sea soon, as a trial run of autonomous ships is underway. Fully autonomous ships will operate based on pre-programmed algorithms to prevent collisions, eliminating the need for onboard navigators or remote operators onshore. Most collision avoidance algorithms are typically based on an engineering approach that predicts the future movement of an approaching ship by observing its vector. However, it is worth noting that even if fully autonomous ships navigate at sea, the majority of ships encountered are still operated by humans. These ships adhere to the Convention on the International Regulations for Preventing Collisions at Sea (COLREG). Therefore, even fully autonomous ships can effectively and legally avoid approaching ships only when they are steered in compliance with the COLREG. However, it has rarely been addressed which procedures should be followed to determine the legally correct action in various situations where fully autonomous ships encounter traditional manned ships. Therefore, this study is divided into two parts. First, a decision-making tree is presented, as simply as possible, to determine the legally correct collision avoidance action according to the COLREG. Secondly, a quantitative analysis is presented for qualitative expressions such as “narrow channel”, “restricted visibility”, and “best aid to avoid collision”. This review will help fully autonomous ships determine legitimate collision avoidance actions and operate safely in seas where human-operated ships are sailing. However, for autonomous ships, the “Trolley problem” and issues related to decision-making for collision avoidance through communication with other ships are left as future challenges. Full article
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14 pages, 5836 KiB  
Article
Overcoming the DDoS Attack Vulnerability of an ISO 19847 Shipboard Data Server
by Changui Lee and Seojeong Lee
J. Mar. Sci. Eng. 2023, 11(5), 1000; https://doi.org/10.3390/jmse11051000 - 08 May 2023
Viewed by 1381
Abstract
The maritime industry, which transports approximately 90% of the world’s goods, plays a crucial role in the global economy. However, increasing reliance on digital technologies has made the industry vulnerable to cybersecurity threats that may compromise the safety and security of maritime operations, [...] Read more.
The maritime industry, which transports approximately 90% of the world’s goods, plays a crucial role in the global economy. However, increasing reliance on digital technologies has made the industry vulnerable to cybersecurity threats that may compromise the safety and security of maritime operations, thereby potentially affecting global supply chain integrity and public safety. This study examines the vulnerability of the ISO 19847:2018 standard shipboard data server to distributed denial-of-service (DDoS) attacks and proposes a method to mitigate this vulnerability. To this end, we propose modifications to the MQTT v5 protocol used by the shipboard data server, which provides streaming data-transfer services, and conduct verification experiments. These modifications allow the shipboard data server to control the frequency of messages published by the MQTT publisher, thereby preventing it from being overwhelmed by massive amounts of traffic in the event of a DDoS attack. Therefore, the proposed method can enhance the overall cybersecurity of the maritime sector by preventing the misuse of onboard MQTT publishers and reducing the impact of DDoS attacks. Full article
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14 pages, 8194 KiB  
Article
A Concept Study on Design Alternatives for Minimizing Accident Consequences in Maritime Autonomous Surface Ships
by Gyeong Joong Lee, Dongkon Lee, Jin Choi and Hee Jin Kang
J. Mar. Sci. Eng. 2023, 11(5), 907; https://doi.org/10.3390/jmse11050907 - 24 Apr 2023
Cited by 2 | Viewed by 1451
Abstract
Autonomous ships, also known as maritime autonomous surface ships (MASS), are vessels that use artificial intelligence and robotics technologies to navigate independently. Due to their advanced technological capabilities, MASS is expected to play a significant role in the future of the shipping industry. [...] Read more.
Autonomous ships, also known as maritime autonomous surface ships (MASS), are vessels that use artificial intelligence and robotics technologies to navigate independently. Due to their advanced technological capabilities, MASS is expected to play a significant role in the future of the shipping industry. The International Maritime Organization (IMO) is currently developing international standards for MASS classification, including accident avoidance technologies. However, the issue of how to mitigate the consequences of accidents involving autonomous ships has not been sufficiently addressed. Therefore, this study focuses on alternative design solutions and emergency response systems for MASS to properly control emergency situations and minimize the impact of accidents, such as flooding and fire on board. The goal is to efficiently address such accidents, especially in situations where the number of people on board is significantly reduced or the ship is being operated automatically or remotely, and to promptly detect and respond to such situations from a remote location. This paper investigates the possibility of modifying the design of the air conditioning system to delay flooding by considering a reduction in the number of crew members in order to prevent flooding. To prevent the spread of fire, the study examines early blockage measures for areas where air can enter. Flood and fire response systems were configured to be executed manually or automatically based on the results of presimulated scenarios defined in thousands of accident scenarios. Each accident propagation and response situation proposes an alternative using a coded shortcut key utilizing graphic symbols of international standard ISO 23120. Full article
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15 pages, 4630 KiB  
Article
State Compensation for Maritime Autonomous Surface Ships’ Remote Control
by Shijun Chen, Xin Xiong, Yuanqiao Wen, Jiaxin Jian and Yamin Huang
J. Mar. Sci. Eng. 2023, 11(2), 450; https://doi.org/10.3390/jmse11020450 - 17 Feb 2023
Cited by 3 | Viewed by 1299
Abstract
With the development of emerging techniques, maritime autonomous surface ships (MASS) have attracted much attention, and the remote control ships’ future seems promising. However, due to communication issues, ship–shore transmission faces the challenge of time delay. The use of the transmitted information without [...] Read more.
With the development of emerging techniques, maritime autonomous surface ships (MASS) have attracted much attention, and the remote control ships’ future seems promising. However, due to communication issues, ship–shore transmission faces the challenge of time delay. The use of the transmitted information without compensation could reduce the effectiveness of controlling or could cause the remote control to be unstable. To eliminate the negative effects of uncertain delays during navigation, an Augmented State Cubature Kalman Filter (AS-CKF) is proposed. First, the uncertainty of the transmission delays is modeled using a probability density function (PDF). Second, the ship’s states are updated and estimated using the delayed observed data, and then the real state of the ship is simultaneously corrected in the augmented state vector. In this way, the delay compensation problem becomes a one-step prediction problem. To test the proposed AS-CKF for MASS, we simulate scenarios with the remote control ship under different communication time delays. The results show improvements compared to the traditional CKF, EKF, or AS-EKF, which indicates the potential of the proposed methods in remote control MASS. Full article
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12 pages, 3554 KiB  
Article
Defense Strategy against False Data Injection Attacks in Ship DC Microgrids
by Hong Zeng, Yuanhao Zhao, Tianjian Wang and Jundong Zhang
J. Mar. Sci. Eng. 2022, 10(12), 1930; https://doi.org/10.3390/jmse10121930 - 06 Dec 2022
Cited by 5 | Viewed by 1466
Abstract
False Data Injection Attacks (FDIA) on ship Direct Current (DC) microgrids may result in the priority trip of a large load, a black-out, and serious accidents of ship collisions when maneuvering in the port. The key of the prevention of FDIA is the [...] Read more.
False Data Injection Attacks (FDIA) on ship Direct Current (DC) microgrids may result in the priority trip of a large load, a black-out, and serious accidents of ship collisions when maneuvering in the port. The key of the prevention of FDIA is the detection of and countermeasures to false data. In this paper, a defense strategy is developed to detect and mitigate against FDIA on ship DC microgrids. First, a DC bus voltage estimator is trained with an Artificial Neural Network (ANN) model. The error between the estimate value and the measure value is compared with a threshold generated from history data to detect the occurrence of FDIA. Combined with the correlation of artificial neural network inputs, bad data are identified and recovered. The method is tested under six cases with different network and physical disturbances in Matlab/Simulink. The results show that the method can identify and mitigate the FDIA effectively; the error of identifying FDIA by ANN is less than 0.5 V. Therefore, the deviation between the actual bus voltage and the reference voltage is less than 0.5 V. Full article
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19 pages, 4985 KiB  
Article
Analysis of Navigator Decision Making through Cognitive Science for the Presentation of a Collision-Avoidance Algorithm for MASSs
by Hee-Jin Lee and Deuk-Jin Park
J. Mar. Sci. Eng. 2022, 10(10), 1420; https://doi.org/10.3390/jmse10101420 - 03 Oct 2022
Viewed by 1231
Abstract
The study of navigator behavior is important for the study of MASSs. This study analyzed navigator behavior through cognitive science, and it modeled the navigator decision-making process. Usually, the assessment of the collision risk for long-distance target ships is conducted through the distance [...] Read more.
The study of navigator behavior is important for the study of MASSs. This study analyzed navigator behavior through cognitive science, and it modeled the navigator decision-making process. Usually, the assessment of the collision risk for long-distance target ships is conducted through the distance (DCPA) and time (TCPA) to the closest point of approach. The navigator’s decision-making process is carried out quantitatively based on numerical values. Although the angle of the rudder is presented as a numerical value (i.e., 5°, 10°, 15°, and so on), it is expected that the navigator’s use of the rudder will depend on the conventional method rather than the quantitative one. Therefore, a scenario was constructed, and a simulation test was carried out through a ship-handling simulator. Our results confirmed that the rudder was used according to the conventional method. Moreover, the navigator decision-making process was analyzed through cognitive science. Cognitive science has revealed that human judgment is not logical, and that all decision making relies on memory. We identified the type of memory that affects the decision making of navigators: the DCPA and navigators’ decision-making-criteria values were mainly formed by episodic memory. A decision-making model for the relationship between the navigator’s episodic memory and the value of the DCPA was subsequently developed. This study took a scientific approach to analyze the process of the decision making of navigators, and an engineering approach to construct a decision-making model for application in MASSs. Full article
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Review

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41 pages, 3346 KiB  
Review
Small Unmanned Surface Vessels—A Review and Critical Analysis of Relations to Safety and Safety Assurance of Larger Autonomous Ships
by Victor Bolbot, Andrei Sandru, Ture Saarniniemi, Otto Puolakka, Pentti Kujala and Osiris A. Valdez Banda
J. Mar. Sci. Eng. 2023, 11(12), 2387; https://doi.org/10.3390/jmse11122387 - 18 Dec 2023
Cited by 1 | Viewed by 2029
Abstract
Autonomous ships represent an emerging paradigm within the maritime sector, poised to bring multiple advantages. Although numerous prototypes have been developed, the deployment of large autonomous ships has predominantly remained confined to domestic waters or specialized military applications. The extensive adoption of autonomous [...] Read more.
Autonomous ships represent an emerging paradigm within the maritime sector, poised to bring multiple advantages. Although numerous prototypes have been developed, the deployment of large autonomous ships has predominantly remained confined to domestic waters or specialized military applications. The extensive adoption of autonomous ships is hampered by several challenges, primarily centered around safety. However, the direct assessment of autonomous technologies on large-scale vessels can be very costly. Small-scale autonomy testing may provide a cheaper option. This study reviews the current small autonomous ship models used by maritime researchers and industry practitioners. It aims to evaluate how these autonomous models currently augment and can augment safety assurances on larger autonomous ships. The review identifies relevant very small Unmanned Surface Vessels (USVs), the main research groups behind them and their applications. Then, the current use of USVs for safety and safety assurance is analyzed. Finally, the paper suggests innovative strategies and research directions for using USVs for the safety assurance of larger autonomous ships. Full article
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Other

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11 pages, 4232 KiB  
Technical Note
The Development of Regional Vessel Traffic Congestion Forecasts Using Hybrid Data from an Automatic Identification System and a Port Management Information System
by Joonbae Son, Dong-Ham Kim, Sang-Woong Yun, Hye-Jin Kim and Sewon Kim
J. Mar. Sci. Eng. 2022, 10(12), 1956; https://doi.org/10.3390/jmse10121956 - 09 Dec 2022
Cited by 6 | Viewed by 1406
Abstract
The present study proposes a new method that forecasts congestion in the area near a port by combining the automatic identification systems of ships and port management information data. The proposed method achieves 85% accuracy for one-day-long ship congestion forecasts. This accuracy level [...] Read more.
The present study proposes a new method that forecasts congestion in the area near a port by combining the automatic identification systems of ships and port management information data. The proposed method achieves 85% accuracy for one-day-long ship congestion forecasts. This accuracy level is high enough to act as a reference value for both manned and unmanned operation situations for autonomous vessels in port areas. The proposed forecast algorithm achieves 95% accuracy when used for a one-hour ship congestion forecast. However, the accuracy of the algorithm is degraded to almost half when the automatic identification system or the port management system is used independently. Full article
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