Intelligent Data-Driven Vessel Trajectory Prediction in Marine Transportation Cyber-Physical System

Liu, Ryan Wen; Liang, Maohan; Nie, Jiangtian; Deng, Xianjun; Xiong, Zehui; Kang, Jiawen; Yang, Helin; Zhang, Yang

doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics53846.2021.00058

Cited by 7 publications

(7 citation statements)

References 38 publications

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“…The proposed maritime accident prediction system has the potential to be applied to various real-world applications, including not only predicting the risk of maritime accidents and preventing potential accidents from happening [71], [72] but also managing and preparing for emergency situations [30], [70]. In addition, predicting maritime accidents would help develop and establish safe and optimal maritime routes for vessels [65], [83], [84]. Furthermore, the system can be effectively used in autonomous vessels and navigation systems [74].…”

Section: Discussionmentioning

confidence: 99%

Development of an Interpretable Maritime Accident Prediction System Using Machine Learning Techniques

Kim

Lim

2022

IEEE Access

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Every year, maritime accidents cause severe damages not only to humans but also to maritime instruments like vessels. The authors of this work therefore propose a machine learning-based maritime accident prediction system that can be used to prevent maritime accidents from happening by predicting and interpreting the accidents. This work overcomes the limitations of the existing works that lack practicability in the sense that the ex-post analyses are conducted to suggest accident prevention strategies but maritime accidents are not analyzed holistically. Using extensive literature reviews and expert interviews, a large number of risk factors associated with maritime accidents are identified, and related data are collected and utilized in the work. Throughout variable selection, data retrieval, hot-spot identification, and the maritime accident prediction model construction process, various machine learning algorithms are exploited in order to construct an organized system. In addition, interpretations for the resulting accident predictions are given using interpretable machine learning algorithms so as to provide explainable results to users. Finally, the proposed system is evaluated using a SERVQUAL model and proves its effectiveness in real-world applications.INDEX TERMS Maritime accident, ocean engineering, accident prediction, interpretable machine learning.

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Section: Discussionmentioning

confidence: 99%

Development of an Interpretable Maritime Accident Prediction System Using Machine Learning Techniques

Kim

Lim

2022

IEEE Access

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“…Researchers have focused on intrusion detection mechanisms [18][19][20], false data injection [18], DoS attacks [21], spoofing [21,22], authentication [23], and the identification of anomalies in water systems [17,19,24]. Machine learning methods have been utilized for anomaly detection and vessel trajectory prediction to improve maritime surveillance [25]. The power domain has seen studies on machine learning-based detection of attacks in water and power grids [15,20,26,27].…”

Section: Security and Privacymentioning

confidence: 99%

“…Financial losses from compromised systems Vessel Trajectory Attacks [25] Spoofing of vessel position or data Manipulating or falsifying vessel position or trajectory data within CPS systems, potentially leading to navigational hazards or unauthorized access.…”

Section: Financial Losses From Data Breachesmentioning

confidence: 99%

“…Liu et al [25] suggest that machine learning algorithms like CNN, LSTM, and hybrid models can effectively predict vessel trajectories by taking into account vessel characteristics, historical movement patterns, and environmental variables, aided by advanced sensor technologies such as AIS and GPS. The SFM-LSTM model combines LSTM with the social force model, providing an accurate and reliable approach for vessel trajectory prediction and enabling smart traffic services in marine transportation systems with the help of AI and IoT technologies.…”

Section: Network-based Threatsmentioning

confidence: 99%

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A Taxonomy of AI Techniques for Security and Privacy in Cyber-Physical Systems

Bandi¹

2023

Preprint

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Background:As cyber-physical systems (CPS) continue to grow, it is progressively crucial to address the challenges of data protection using artificial intelligence (AI). Objective:The goal of this research is to provide an up-to-date overview of the security and privacy issues of CPS that incorporate AI techniques. Method:To achieve this, the author conducted a systematic literature review, focusing on 35 relevant articles. Results:The data collected from these studies was then categorized into three main areas: 1) different security and privacy issues, 2) application areas and vulnerabilities, and 3) the AI techniques used to address security and privacy concerns. The literature review highlights that intrusion detection and cyberattacks are the most commonly studied areas in CPS, while Machine Learnning (ML)-based attacks and vessel trajectory are less explored. The review identifies various CPS applications such as water treatment, energy, healthcare, and transportation that address security and privacy concerns. However, a relatively small proportion of studies focused on the manufacturing domain. The review also notes that while supervised machine learning algorithms under the classification category are commonly used to address data protection issues, there are comparatively fewer studies that have implemented automation processes using robots and deep learning. Limitations:The articles related to blockchain-based research were not included in this review to focus solely on AI techniques. Conclusion:The results of this analysis indicate that there is a significant need for innovative AI/ML techniques to protect intelligent systems and networks from ML-based security threats.

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“…Researchers have focused on intrusion detection mechanisms [18][19][20], false data injection [20], denial of service (DoS) attacks [21], spoofing [21,22], authentication [22], and the identification of anomalies in water systems [17,18,23]. ML methods have been utilized for anomaly detection and vessel trajectory prediction to improve maritime surveillance [24]. The power domain has seen studies on MLbased detection of attacks in water and power grids [15,19,25,26].…”

Section: Introductionmentioning

confidence: 99%

A Taxonomy of AI Techniques for Security and Privacy in Cyber–Physical Systems

Bandi

2024

JCCE

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This research paper addresses the concerns related to security and privacy in cyber-physical systems (CPS) and explores the role of artificial intelligence (AI) in addressing these concerns. This paper presents a comprehensive classification of various security and privacy threats in CPS, providing an organized overview of potential risks, economic loss, and enabling effective risk assessment. This paper highlights how AI can help address the security and privacy concerns in CPS by presenting a detailed flow chart that illustrates the stepby-step process of using AI and machine learning (ML) techniques to detect security and privacy issues. This integrated approach serves as a guide for designing ML-based secure CPS, enabling proactive defense mechanisms and improving incident response and recovery. Furthermore, the research explores the various AI techniques that can be employed to address security and privacy concerns in CPS. A taxonomy of ML techniques specifically relevant to security and privacy issues is provided, offering insights into the potential applications of these techniques. In conclusion, this research emphasizes the significance of addressing security and privacy concerns in CPS and highlights the role of AI in tackling these challenges.

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Intelligent Data-Driven Vessel Trajectory Prediction in Marine Transportation Cyber-Physical System

Cited by 7 publications

References 38 publications

Development of an Interpretable Maritime Accident Prediction System Using Machine Learning Techniques

Development of an Interpretable Maritime Accident Prediction System Using Machine Learning Techniques

A Taxonomy of AI Techniques for Security and Privacy in Cyber-Physical Systems

A Taxonomy of AI Techniques for Security and Privacy in Cyber–Physical Systems

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