@inproceedings{378844032d0f4afa86eeeae3bfb4ddcb,
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.",
keywords = "Big data, Data cyber-security, Machine learning, Maritime, Statistics",
author = "Saeed Rahimpour and Mahtab Shahin and Yigit G{\"u}lmez and Sanja Bauk",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; 10th International Congress on Information and Communication Technology, ICICT 2025 ; Conference date: 18-02-2025 Through 21-02-2025",
year = "2025",
doi = "10.1007/978-981-96-6935-6\_39",
language = "English",
isbn = "9789819669349",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "471--483",
editor = "Xin-She Yang and Sherratt, \{R. Simon\} and Nilanjan Dey and Amit Joshi",
booktitle = "Proceedings of 10th International Congress on Information and Communication Technology - ICICT 2025",
address = "Germany",
}