Shipping performed by contemporary vessels is the backbone of global trade. Modern vessels are equipped with many computerized systems to enhance safety and operational efficiency. One such system developed is the integrated navigation system (INS), which combines information and functions for the bridge team onboard. An INS comprises many marine components involving cyber threats and vulnerabilities. This study aims to assess the cyber risks of such components. To this end, a methodology considering the MITRE ATT&CK framework, which provides adversarial tactics, techniques, and mitigation measures, was applied by modifying for cyber risks at sea. We assessed cyber risks of 25 components on the bridge by implementing the extended methodology in this study. As a result of the assessment, we found 1850 risks. We classified our results as 1805 low, 32 medium, 9 high, and 4 critical levels for 22 components. Three components did not include any cyber risks. Scientists, ship operators, and product developers could use the findings to protect navigation systems onboard from potential cyber threats and vulnerabilities.
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http://dx.doi.org/10.3390/s22228745 | DOI Listing |
Front Psychol
December 2024
Department of Psychology, Indiana State University, Terra Haute, IN, United States.
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View Article and Find Full Text PDFData Brief
December 2024
Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
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View Article and Find Full Text PDFCyberpsychol Behav Soc Netw
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School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia.
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View Article and Find Full Text PDFHeliyon
December 2024
Doctoral School on Safety and Security Sciences, Óbuda University, 1034, Budapest, Hungary.
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View Article and Find Full Text PDFAggress Behav
January 2025
Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, China.
The general aggression model (GAM) suggests that cyber-aggression stems from individual characteristics and situational contexts. Previous studies have focused on limited factors using linear models, leading to oversimplified predictions. This study used the light gradient boosting machine (LightGBM) to identify and rank the importance of various risk and protective factors in cyber-aggression.
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