The maritime sector is increasingly integrating Information and Communication Technology (ICT) and Artificial Intelligence (AI) technologies to enhance safety, environmental protection, and operational efficiency. With the introduction of the MASS Code by the International Maritime Organization (IMO), which regulates Maritime Autonomous Surface Ships (MASS), ensuring the safety of AI-integrated systems on these vessels has become critical. To achieve safe navigation, it is essential to identify potential risks during the system planning stage and design systems that can effectively address these risks. This paper proposes RA4MAIS (Risk Assessment for Maritime Artificial Intelligence Safety), a risk identification method specifically useful for developing AI-integrated maritime systems. RA4MAIS employs a systematic approach to uncover potential risks by considering internal system failures, human interactions, environmental conditions, AI-specific characteristics, and data quality issues. The method provides structured guidance to identify unknown risk situations and supports the development of safety requirements that guide system design and implementation. A case study on an Electronic Chart Display and Information System (ECDIS) with an AI-integrated collision avoidance function demonstrates the applicability of RA4MAIS, highlighting its effectiveness in identifying specific risks related to AI performance and reliability. The proposed method offers a foundational step towards enhancing the safety of software systems, contributing to the safe operation of autonomous ships.

Artificial intelligence (AI) will play an important role in realizing maritime autonomous surface ships (MASSs). However, as a double-edged sword, this new technology brings forth new threats. The purpose of this study is to raise awareness among stakeholders regarding the potential security threats posed by AI in MASSs. To achieve this, we propose a hypothetical attack scenario in which a clean-label poisoning attack was executed on an object detection model, which resulted in boats being misclassified as ferries, thus preventing the detection of pirates approaching a boat. We used the poison frog algorithm to generate poisoning instances, and trained a YOLOv5 model with both clean and poisoned data. Despite the high accuracy of the model, it misclassified boats as ferries owing to the poisoning of the target instance. Although the experiment was conducted under limited conditions, we confirmed vulnerabilities in the object detection algorithm. This misclassification could lead to inaccurate AI decision making and accidents. The hypothetical scenario proposed in this study emphasizes the vulnerability of object detection models to clean-label poisoning attacks, and the need for mitigation strategies against security threats posed by AI in the maritime industry.

The development of artificial intelligence (AI) technologies, such as machine learning algorithms, computer vision systems, and sensors, has allowed maritime autonomous surface ships (MASS) to navigate, detect and avoid obstacles, and make real-time decisions based on their environment. Despite the benefits of AI in MASS, its potential security threats must be considered. An adversarial attack is a security threat that involves manipulating the training data of a model to compromise its accuracy and reliability. This study focuses on security threats faced by a deep neural network-based object classification algorithm, particularly you only look once version 5 (YOLOv5), which is a model used for object classification. We performed transfer learning on YOLOv5 and tested various adversarial attack methods. We conducted experiments using four types of adversarial attack methods and parameter changes to determine the attacks that could be detrimental to YOLOv5. Through this study, we aim to raise awareness of the vulnerability of AI algorithms for object detection to adversarial attacks and emphasize the need for efforts to overcome them; these efforts can contribute to safe navigation in MASS.

NAVTEX is a key component in the Global Maritime Distress and Safety System (GMDSS) that automatically transmits urgent maritime safety information such as navigational and meteorological warnings and forecasts to vessels. For the safe navigation of smart ships, this information from different systems should be shared harmoniously in the Common Maritime Data Structure (CMDS). To share NAVTEX messages as CMDS, words in NAVTEX messages must be semantically classified and placed within the CMDS structure. While traditional parsing methods are typically used to understand message semantics, NAVTEX requires natural language processing methods with deep learning due to its unstructured messages. This paper applies six types of Bi-LSTM CRF-based deep learning models to NAVTEX navigational safety messages and analyzes the results to find the most suitable model for understanding the semantics of each word in NAVTEX messages. This technique can be applied to accurately convey the meaning of NAVTEX navigational safety messages to equipment that requires navigational safety information on smart ships without human intervention.

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.

The International Maritime Organization (IMO) introduced the Maritime Service Portfolio (MSP) and Maritime Single Window (MSW) to enhance the digitalization and efficiency of maritime transportation. While the MSP defines 16 maritime services focused on safety, security, efficiency, and environmental protection, the MSW provides a unified digital platform for submitting and processing information related to a ship’s operations. To support the implementation of MSW, the IMO Compendium provides standardized data sets and reference models to enable seamless information exchange across maritime systems. This paper proposes an expansion of the IMO Compendium to integrate the MSP’s maritime safety information service into the MSW environment. The study focuses on the integration of NAVTEX messages, a key source of navigational safety information, by identifying their key attributes and structuring them according to the IHO S-124 standard. A case study demonstrates the feasibility of the proposed data structure by transforming a sample NAVTEX message into the expanded IMO Compendium format and testing its transmission using an open-source MQTT library. This paper provides a structured methodology for integrating NAVTEX messages, effectively bridging legacy systems with modern digital infrastructures and facilitating enhanced interoperability in maritime operations. The proposed data structure will be presented to standardization bodies for further consideration, contributing to ongoing efforts to improve maritime operational efficiency and support digital transformation.