Decoding dependencies among the risk factors influencing maritime cybersecurity: Lessons learned from historical incidents in the past two decades

被引:4
作者
Mohsendokht, Massoud [1 ]
Li, Huanhuan [1 ]
Kontovas, Christos [1 ]
Chang, Chia-Hsun [1 ]
Qu, Zhuohua [2 ]
Yang, Zaili [1 ]
机构
[1] Liverpool John Moores Univ, Liverpool Logist Offshore & Marine LOOM Res Inst, Liverpool, England
[2] Liverpool John Moores Univ, Liverpool Business Sch, Liverpool, England
基金
欧洲研究理事会;
关键词
Maritime security; Cyber-attacks; Bayesian network; Risk analysis; Cybersecurity; AUGMENTED NAIVE BAYES; CYBER SECURITY; NETWORK; FRAMEWORK; AGREEMENT; KNOWLEDGE; PIRACY; SAFETY; MODEL;
D O I
10.1016/j.oceaneng.2024.119078
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
The distinctive features of maritime infrastructures present significant challenges in terms of security. Disruptions to the normal functioning of any part of maritime transportation can have wide-ranging consequences at both national and international levels, making it an attractive target for malicious attacks. Within this context, the integration of digitalization and technological advancements in seaports, vessels and other maritime elements exposes them to cyber threats. In response to this critical challenge, this paper aims to formulate a novel cybersecurity risk analysis method for ensuring maritime security. This approach is based on a data-driven Bayesian network, utilizing recorded cyber incidents spanning the past two decades. The findings contribute to the identification of highly significant contributing factors, a meticulous examination of their nature, the revelation of their interdependencies, and the estimation of their probabilities of occurrence. Rigorous validation of the developed model ensures its robustness for both diagnostic and prognostic purposes. The implications drawn from this study offer valuable insights for stakeholders and governmental bodies, enhancing their understanding of how to address cyber threats affecting the maritime industry. This knowledge aids in the implementation of necessary preventive measures.
引用
收藏
页数:19
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共 91 条
  • [1] Safe Information Exchange on Board of the Ship
    Ahvenjarvi, S.
    Czarnowski, I
    Kala, J.
    Kyster, A.
    Meyer, I
    Mogensen, J.
    Szyman, P.
    [J]. TRANSNAV-INTERNATIONAL JOURNAL ON MARINE NAVIGATION AND SAFETY OF SEA TRANSPORTATION, 2019, 13 (01) : 165 - 171
  • [2] Cyber security in marine transport: opportunities and legal challenges
    Al Ali, Naser Abdel Raheem
    Chebotareva, Anna A.
    Chebotarev, Vladimir E.
    [J]. POMORSTVO-SCIENTIFIC JOURNAL OF MARITIME RESEARCH, 2021, 35 (02) : 248 - 255
  • [3] Advanced uncertainty modelling for container port risk analysis
    Alyami, Hani
    Yang, Zaili
    Riahi, Ramin
    Bonsall, Stephen
    Wang, Jin
    [J]. ACCIDENT ANALYSIS AND PREVENTION, 2019, 123 : 411 - 421
  • [4] Exploiting Experts' Knowledge for Structure Learning of Bayesian Networks
    Amirkhani, Hossein
    Rahmati, Mohammad
    Lucas, Peter J. F.
    Hommersom, Arjen
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2017, 39 (11) : 2154 - 2170
  • [5] [Anonymous], 2019, Netica (version 607)
  • [6] [Anonymous], 2018, The guidelines on cyber security onboard ships, V3rd
  • [7] A Survey on Cyber Security Threats in IoT-Enabled Maritime Industry
    Ashraf, Imran
    Park, Yongwan
    Hur, Soojung
    Kim, Sung Won
    Alroobaea, Roobaea
    Bin Zikria, Yousaf
    Nosheen, Summera
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2023, 24 (02) : 2677 - 2690
  • [8] Cyber Security in the Maritime Industry: A Systematic Survey of Recent Advances and Future Trends
    Ben Farah, Mohamed Amine
    Ukwandu, Elochukwu
    Hindy, Hanan
    Brosset, David
    Bures, Miroslav
    Andonovic, Ivan
    Bellekens, Xavier
    [J]. INFORMATION, 2022, 13 (01)
  • [9] Ransomware on cyber-physical systems: Taxonomies, case studies, security gaps, and open challenges
    Benmalek M.
    [J]. Internet of Things and Cyber-Physical Systems, 2024, 4 : 186 - 202
  • [10] EL-NAHL: Exploring labels autoencoding in augmented hidden layers of feedforward neural networks for cybersecurity in smart grids
    Berghout, Tarek
    Benbouzid, Mohamed
    [J]. RELIABILITY ENGINEERING & SYSTEM SAFETY, 2022, 226