Data Mining and Cybersecurity-Driven Solutions for CO2 Emissions Reduction of Different Maritime Shipping: A Multi-faceted Analysis

Saeed Rahimpour; Mahtab Shahin; Yigit Gülmez; Sanja Bauk

doi:10.1007/978-981-96-6935-6_39

Data Mining and Cybersecurity-Driven Solutions for CO₂ Emissions Reduction of Different Maritime Shipping: A Multi-faceted Analysis

Saeed Rahimpour^*
, Mahtab Shahin
, Yigit Gülmez
, Sanja Bauk

^*Corresponding author for this work

Not published at VTT

Tallinn University of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

1 Citation (Scopus)

Abstract

Using advanced data mining techniques, specifically association rule mining (ARM) and clustering, this study presents a novel approach to maritime CO₂ emissions analysis, revealing hidden patterns and relationships that traditional statistical models cannot capture. Various ship types can be analyzed for operational and technical efficiency to provide actionable insights into reducing emissions. In addition, robust cybersecurity measures are integrated to ensure the integrity and reliability of the data, allowing compliant and secure decision-making. The findings indicate that oil tankers and LNG carriers, which emit significant amounts of pollution, are prime candidates for retrofitting and implementing cleaner technologies in the near future.

Original language	English
Title of host publication	Proceedings of 10th International Congress on Information and Communication Technology - ICICT 2025
Subtitle of host publication	ICICT 2025, London, Volume 4
Editors	Xin-She Yang, R. Simon Sherratt, Nilanjan Dey, Amit Joshi
Publisher	Springer
Pages	471-483
Number of pages	13
ISBN (Print)	9789819669349
DOIs	https://doi.org/10.1007/978-981-96-6935-6_39
Publication status	Published - 2025
MoE publication type	A4 Article in a conference publication
Event	10th International Congress on Information and Communication Technology, ICICT 2025 - London, United Kingdom Duration: 18 Feb 2025 → 21 Feb 2025

Publication series

Series	Lecture Notes in Networks and Systems
Volume	1443 LNNS
ISSN	2367-3370

Conference

Conference	10th International Congress on Information and Communication Technology, ICICT 2025
Country/Territory	United Kingdom
City	London
Period	18/02/25 → 21/02/25

Funding

Research for this publication was funded by the EU Horizon2020 project 952360-MariCybERA.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 13 Climate Action
SDG 14 Life Below Water

Keywords

Big data
Data cyber-security
Machine learning
Maritime
Statistics

Access to Document

10.1007/978-981-96-6935-6_39

Cite this

Rahimpour, S., Shahin, M., Gülmez, Y., & Bauk, S. (2025). Data Mining and Cybersecurity-Driven Solutions for CO₂ Emissions Reduction of Different Maritime Shipping: A Multi-faceted Analysis. In X.-S. Yang, R. S. Sherratt, N. Dey, & A. Joshi (Eds.), Proceedings of 10th International Congress on Information and Communication Technology - ICICT 2025: ICICT 2025, London, Volume 4 (pp. 471-483). Springer. https://doi.org/10.1007/978-981-96-6935-6_39

Rahimpour, Saeed ; Shahin, Mahtab ; Gülmez, Yigit et al. / Data Mining and Cybersecurity-Driven Solutions for CO₂ Emissions Reduction of Different Maritime Shipping : A Multi-faceted Analysis. Proceedings of 10th International Congress on Information and Communication Technology - ICICT 2025: ICICT 2025, London, Volume 4. editor / Xin-She Yang ; R. Simon Sherratt ; Nilanjan Dey ; Amit Joshi. Springer, 2025. pp. 471-483 (Lecture Notes in Networks and Systems, Vol. 1443 LNNS).

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title = "Data Mining and Cybersecurity-Driven Solutions for CO2 Emissions Reduction of Different Maritime Shipping: A Multi-faceted Analysis",

abstract = "Using advanced data mining techniques, specifically association rule mining (ARM) and clustering, this study presents a novel approach to maritime CO2 emissions analysis, revealing hidden patterns and relationships that traditional statistical models cannot capture. Various ship types can be analyzed for operational and technical efficiency to provide actionable insights into reducing emissions. In addition, robust cybersecurity measures are integrated to ensure the integrity and reliability of the data, allowing compliant and secure decision-making. The findings indicate that oil tankers and LNG carriers, which emit significant amounts of pollution, are prime candidates for retrofitting and implementing cleaner technologies in the near future.",

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Rahimpour, S, Shahin, M, Gülmez, Y & Bauk, S 2025, Data Mining and Cybersecurity-Driven Solutions for CO₂ Emissions Reduction of Different Maritime Shipping: A Multi-faceted Analysis. in X-S Yang, RS Sherratt, N Dey & A Joshi (eds), Proceedings of 10th International Congress on Information and Communication Technology - ICICT 2025: ICICT 2025, London, Volume 4. Springer, Lecture Notes in Networks and Systems, vol. 1443 LNNS, pp. 471-483, 10th International Congress on Information and Communication Technology, ICICT 2025, London, United Kingdom, 18/02/25. https://doi.org/10.1007/978-981-96-6935-6_39

Data Mining and Cybersecurity-Driven Solutions for CO₂ Emissions Reduction of Different Maritime Shipping: A Multi-faceted Analysis. / Rahimpour, Saeed; Shahin, Mahtab; Gülmez, Yigit et al.
Proceedings of 10th International Congress on Information and Communication Technology - ICICT 2025: ICICT 2025, London, Volume 4. ed. / Xin-She Yang; R. Simon Sherratt; Nilanjan Dey; Amit Joshi. Springer, 2025. p. 471-483 (Lecture Notes in Networks and Systems, Vol. 1443 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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AB - Using advanced data mining techniques, specifically association rule mining (ARM) and clustering, this study presents a novel approach to maritime CO2 emissions analysis, revealing hidden patterns and relationships that traditional statistical models cannot capture. Various ship types can be analyzed for operational and technical efficiency to provide actionable insights into reducing emissions. In addition, robust cybersecurity measures are integrated to ensure the integrity and reliability of the data, allowing compliant and secure decision-making. The findings indicate that oil tankers and LNG carriers, which emit significant amounts of pollution, are prime candidates for retrofitting and implementing cleaner technologies in the near future.

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Rahimpour S, Shahin M, Gülmez Y, Bauk S. Data Mining and Cybersecurity-Driven Solutions for CO₂ Emissions Reduction of Different Maritime Shipping: A Multi-faceted Analysis. In Yang XS, Sherratt RS, Dey N, Joshi A, editors, Proceedings of 10th International Congress on Information and Communication Technology - ICICT 2025: ICICT 2025, London, Volume 4. Springer. 2025. p. 471-483. (Lecture Notes in Networks and Systems, Vol. 1443 LNNS). doi: 10.1007/978-981-96-6935-6_39