Academic Journal
Mechanisms for Securing Autonomous Shipping Services and Machine Learning Algorithms for Misbehaviour Detection.
| Τίτλος: | Mechanisms for Securing Autonomous Shipping Services and Machine Learning Algorithms for Misbehaviour Detection. |
|---|---|
| Συγγραφείς: | Haruna, Marwan, Gebremeskel, Kaleb Gebremichael, Troscia, Martina, Tardo, Alexandr, Pagano, Paolo |
| Πηγή: | Telecom; Dec2024, Vol. 5 Issue 4, p1031-1050, 20p |
| Θεματικοί όροι: | Machine learning, Artificial intelligence, Digital technology, Infrastructure (Economics), Harbors |
| Περίληψη: | Technological developments within the maritime sector are resulting in rapid progress that will see the commercial use of autonomous vessels, known as Maritime Autonomous Surface Ships (MASSs). Such ships are equipped with a range of advanced technologies, such as IoT devices, artificial intelligence (AI) systems, machine learning (ML)-based algorithms, and augmented reality (AR) tools. Through such technologies, the autonomous vessels can be remotely controlled from Shore Control Centres (SCCs) by using real-time data to optimise their operations, enhance safety, and reduce the possibility of human error. Apart from the regulatory aspects, which are under definition by the International Maritime Organisation (IMO), cybersecurity vulnerabilities must be considered and properly addressed to prevent such complex systems from being tampered with. This paper proposes an approach that operates on two different levels to address cybersecurity. On one side, our solution is intended to secure communication channels between the SCCs and the vessels using Secure Exchange and COMmunication (SECOM) standard; on the other side, it aims to secure the underlying digital infrastructure in charge of data collection, storage and processing by relying on a set of machine learning (ML) algorithms for anomaly and intrusion detection. The proposed approach is validated against a real implementation of the SCC deployed in the Livorno seaport premises. Finally, the experimental results and the performance evaluation are provided to assess its effectiveness accordingly. [ABSTRACT FROM AUTHOR] |
| Copyright of Telecom is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 181942816 RelevancyScore: 983 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 983.441223144531 |
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| Items | – Name: Title Label: Title Group: Ti Data: Mechanisms for Securing Autonomous Shipping Services and Machine Learning Algorithms for Misbehaviour Detection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Haruna%2C+Marwan%22">Haruna, Marwan</searchLink><br /><searchLink fieldCode="AR" term="%22Gebremeskel%2C+Kaleb+Gebremichael%22">Gebremeskel, Kaleb Gebremichael</searchLink><br /><searchLink fieldCode="AR" term="%22Troscia%2C+Martina%22">Troscia, Martina</searchLink><br /><searchLink fieldCode="AR" term="%22Tardo%2C+Alexandr%22">Tardo, Alexandr</searchLink><br /><searchLink fieldCode="AR" term="%22Pagano%2C+Paolo%22">Pagano, Paolo</searchLink> – Name: TitleSource Label: Source Group: Src Data: Telecom; Dec2024, Vol. 5 Issue 4, p1031-1050, 20p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+technology%22">Digital technology</searchLink><br /><searchLink fieldCode="DE" term="%22Infrastructure+%28Economics%29%22">Infrastructure (Economics)</searchLink><br /><searchLink fieldCode="DE" term="%22Harbors%22">Harbors</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Technological developments within the maritime sector are resulting in rapid progress that will see the commercial use of autonomous vessels, known as Maritime Autonomous Surface Ships (MASSs). Such ships are equipped with a range of advanced technologies, such as IoT devices, artificial intelligence (AI) systems, machine learning (ML)-based algorithms, and augmented reality (AR) tools. Through such technologies, the autonomous vessels can be remotely controlled from Shore Control Centres (SCCs) by using real-time data to optimise their operations, enhance safety, and reduce the possibility of human error. Apart from the regulatory aspects, which are under definition by the International Maritime Organisation (IMO), cybersecurity vulnerabilities must be considered and properly addressed to prevent such complex systems from being tampered with. This paper proposes an approach that operates on two different levels to address cybersecurity. On one side, our solution is intended to secure communication channels between the SCCs and the vessels using Secure Exchange and COMmunication (SECOM) standard; on the other side, it aims to secure the underlying digital infrastructure in charge of data collection, storage and processing by relying on a set of machine learning (ML) algorithms for anomaly and intrusion detection. The proposed approach is validated against a real implementation of the SCC deployed in the Livorno seaport premises. Finally, the experimental results and the performance evaluation are provided to assess its effectiveness accordingly. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Telecom is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/telecom5040053 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 1031 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Digital technology Type: general – SubjectFull: Infrastructure (Economics) Type: general – SubjectFull: Harbors Type: general Titles: – TitleFull: Mechanisms for Securing Autonomous Shipping Services and Machine Learning Algorithms for Misbehaviour Detection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Haruna, Marwan – PersonEntity: Name: NameFull: Gebremeskel, Kaleb Gebremichael – PersonEntity: Name: NameFull: Troscia, Martina – PersonEntity: Name: NameFull: Tardo, Alexandr – PersonEntity: Name: NameFull: Pagano, Paolo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 26734001 Numbering: – Type: volume Value: 5 – Type: issue Value: 4 Titles: – TitleFull: Telecom Type: main |
| ResultId | 1 |