IDEAS home Printed from https://ideas.repec.org/a/eee/soceps/v87y2023ipbs0038012123000848.html
   My bibliography  Save this article

Network models for cyber attacks evaluation

Author

Listed:
  • Facchinetti, Silvia
  • Osmetti, Silvia Angela
  • Tarantola, Claudia

Abstract

The significant recent growth in digitization has been accompanied by a rapid increase in cyber attacks affecting all sectors. Thus, it is fundamental to make a correct assessment of the risk to suffer a cyber attack and of the resulting damage. Quantitative loss data are rarely available, while it is possible to obtain a qualitative evaluation on an ordinal scale of the gravity of an attack from experts of the sector. In this paper, we discuss how network models can be useful instruments for the evaluation of the risk associated to a cyber attack. In particular, we consider Bayesian Networks, Random Forests and Social Networks to study different aspects of the examined problem. Along with the description of the methodology, we examine a real set of data regarding serious cyber attacks occurred worldwide before and during the pandemic due to Covid-19. In the analysis, we also investigate how the Covid-19 period had an impact on the cyber risk landscape in terms of frequency and gravity of the observed attacks.

Suggested Citation

  • Facchinetti, Silvia & Osmetti, Silvia Angela & Tarantola, Claudia, 2023. "Network models for cyber attacks evaluation," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    • Handle: RePEc:eee:soceps:v:87:y:2023:i:pb:s0038012123000848
    DOI: 10.1016/j.seps.2023.101584
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0038012123000848
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.seps.2023.101584?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gert Sabidussi, 1966. "The centrality index of a graph," Psychometrika, Springer;The Psychometric Society, vol. 31(4), pages 581-603, December.
    2. Anil K. Kashyap & Anne Wetherilt, 2019. "Some Principles for Regulating Cyber Risk," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 482-487, May.
    3. Dalla Valle, L. & Giudici, P., 2008. "A Bayesian approach to estimate the marginal loss distributions in operational risk management," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3107-3127, February.
    4. Luca Allodi & Fabio Massacci, 2017. "Security Events and Vulnerability Data for Cybersecurity Risk Estimation," Risk Analysis, John Wiley & Sons, vol. 37(8), pages 1606-1627, August.
    5. Claudia Tarantola & Paola Vicard & Ioannis Ntzoufras, 2012. "Monitoring and Improving Greek Banking Services Using Bayesian Networks: an Analysis of Mystery Shopping Data," Quaderni di Dipartimento 160, University of Pavia, Department of Economics and Quantitative Methods.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. De Masi, G. & Giovannetti, G. & Ricchiuti, G., 2013. "Network analysis to detect common strategies in Italian foreign direct investment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1202-1214.
    2. Iñaki Aldasoro & Leonardo Gambacorta & Paolo Giudici & Thomas Leach, 2023. "Operational and Cyber Risks in the Financial Sector," International Journal of Central Banking, International Journal of Central Banking, vol. 19(5), pages 340-402, December.
    3. Fogel, Kathy & Jandik, Tomas & McCumber, William R., 2018. "CFO social capital and private debt," Journal of Corporate Finance, Elsevier, vol. 52(C), pages 28-52.
    4. F. Cugnata & G. Perucca & S. Salini, 2017. "Bayesian networks and the assessment of universities' value added," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(10), pages 1785-1806, July.
    5. Hyuk-Soo Kwon & Jihong Lee & Sokbae Lee & Ryungha Oh, 2022. "Knowledge spillovers and patent citations: trends in geographic localization, 1976–2015," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 31(3), pages 123-147, April.
    6. José Ramón Martínez Resano, 2022. "Digital resilience and financial stability. The quest for policy tools in the financial sector," Financial Stability Review, Banco de España, issue Autumn.
    7. Mengying Cui & David Levinson, 2018. "Accessibility analysis of risk severity," Transportation, Springer, vol. 45(4), pages 1029-1050, July.
    8. Zheng, Wei & Wei, Sheng, 2024. "A ‘node-place-network-city’ framework to examine HSR station area development dynamics: Station typologies and development strategies," Journal of Transport Geography, Elsevier, vol. 120(C).
    9. Fang, Ming & Francis, Bill & Hasan, Iftekhar & Wu, Qiang, 2022. "External social networks and earnings management," The British Accounting Review, Elsevier, vol. 54(2).
    10. Mario V. Tomasello & Mauro Napoletano & Antonios Garas & Frank Schweitzer, 2017. "The rise and fall of R&D networks," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 26(4), pages 617-646.
    11. Tao, Qizhi & Li, Haoyu & Wu, Qun & Zhang, Ting & Zhu, Yingjun, 2019. "The dark side of board network centrality: Evidence from merger performance," Journal of Business Research, Elsevier, vol. 104(C), pages 215-232.
    12. repec:dau:papers:123456789/7616 is not listed on IDEAS
    13. Jackie Krafft & Francesco Quatraro, 2011. "The Dynamics of Technological Knowledge: From Linearity to Recombination," Chapters, in: Cristiano Antonelli (ed.), Handbook on the Economic Complexity of Technological Change, chapter 7, Edward Elgar Publishing.
    14. Thomas M. Eisenbach & Anna Kovner & Michael Junho Lee, 2025. "When It Rains, It Pours: Cyber Vulnerability and Financial Conditions," Economic Policy Review, Federal Reserve Bank of New York, vol. 31(1), pages 1-24, January.
    15. Prosper D Akrobotu & Tamsin E James & Christian F A Negre & Susan M Mniszewski, 2022. "A QUBO formulation for top-τ eigencentrality nodes," PLOS ONE, Public Library of Science, vol. 17(7), pages 1-18, July.
    16. Zhao, Shuying & Sun, Shaowei, 2023. "Identification of node centrality based on Laplacian energy of networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    17. Wu, Tao & Xian, Xingping & Zhong, Linfeng & Xiong, Xi & Stanley, H. Eugene, 2018. "Power iteration ranking via hybrid diffusion for vital nodes identification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 802-815.
    18. Giulia Masi & Giorgio Ricchiuti, 2020. "From FDI network topology to macroeconomic instability," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(1), pages 133-158, January.
    19. Wang, Feifei & Sun, Zejun & Gan, Quan & Fan, Aiwan & Shi, Hesheng & Hu, Haifeng, 2022. "Influential node identification by aggregating local structure information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    20. Zamudio, César & Anokhin, Sergey & Kellermanns, Franz W., 2014. "Network analysis: A concise review and suggestions for family business research," Journal of Family Business Strategy, Elsevier, vol. 5(1), pages 63-71.
    21. Pan Han-huai & Wang Lin-wei & Liu Hao & MohammadJavad Abdollahi, 2025. "Identifying influential nodes in complex networks: a semi-local centrality measure based on augmented graph and average shortest path theory," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 88(1), pages 1-18, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:soceps:v:87:y:2023:i:pb:s0038012123000848. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/seps .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.