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Finding critical nodes in infrastructure networks

Author

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  • Faramondi, Luca
  • Setola, Roberto
  • Panzieri, Stefano
  • Pascucci, Federica
  • Oliva, Gabriele

Abstract

It is well known that profiling attacker behavior is an effective way to obtain insights into network attacks and to identify the systems and components that must be protected. This paper presents a novel integer linear programming formulation that models the strategy of an attacker who targets a set of nodes with the goal of compromising or destroying them. The attacker model considers the infliction of the greatest possible damage with minimal attacker effort. Specifically, it is assumed that the attacker is guided by three conflicting objectives: (i) maximization of the number of disconnected components; (ii) minimization of the size of the largest connected component; and (iii) minimization of the attack cost. Compared with other research in the area, the proposed formulation is much more descriptive but has less complexity; thus, it is very useful for predicting attacks and identifying the entities that must be protected. Since exact solutions of the formulation are computationally expensive for large problems, a heuristic algorithm is presented to obtain approximate solutions. Simulation results using a U.S. airport network dataset demonstrate the effectiveness and utility of the proposed approach.

Suggested Citation

  • Faramondi, Luca & Setola, Roberto & Panzieri, Stefano & Pascucci, Federica & Oliva, Gabriele, 2018. "Finding critical nodes in infrastructure networks," International Journal of Critical Infrastructure Protection, Elsevier, vol. 20(C), pages 3-15.
  • Handle: RePEc:eee:ijocip:v:20:y:2018:i:c:p:3-15
    DOI: 10.1016/j.ijcip.2017.11.004
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    References listed on IDEAS

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    Citations

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    Cited by:

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    3. Wang, Shuliang & Sun, Jingya & Zhang, Jianhua & Dong, Qiqi & Gu, Xifeng & Chen, Chen, 2023. "Attack-Defense game analysis of critical infrastructure network based on Cournot model with fixed operating nodes," International Journal of Critical Infrastructure Protection, Elsevier, vol. 40(C).
    4. Jiang, Cheng & Liu, Zhonghua, 2019. "Detecting multiple key players under the positive effect by using a distance-based connectivity approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    5. Goldbeck, Nils & Angeloudis, Panagiotis & Ochieng, Washington Y., 2019. "Resilience assessment for interdependent urban infrastructure systems using dynamic network flow models," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 62-79.
    6. Li, Yulong & Lin, Jie & Zhang, Chi & Zhu, Huaxing & Zeng, Saixing & Sun, Chengshaung, 2022. "Joint optimization of structure and protection of interdependent infrastructure networks," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    7. Hassan Al-Zarooni & Hamdi Bashir, 0. "An integrated ISM fuzzy MICMAC approach for modeling and analyzing electrical power system network interdependencies," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 0, pages 1-23.
    8. Xiaoqian Sun & Sebastian Wandelt, 2021. "Robustness of Air Transportation as Complex Networks:Systematic Review of 15 Years of Research and Outlook into the Future," Sustainability, MDPI, vol. 13(11), pages 1-19, June.
    9. Hassan Al-Zarooni & Hamdi Bashir, 2020. "An integrated ISM fuzzy MICMAC approach for modeling and analyzing electrical power system network interdependencies," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(6), pages 1204-1226, December.
    10. Sándor, Hunor & Genge, Béla & Szántó, Zoltán & Márton, Lőrinc & Haller, Piroska, 2019. "Cyber attack detection and mitigation: Software Defined Survivable Industrial Control Systems," International Journal of Critical Infrastructure Protection, Elsevier, vol. 25(C), pages 152-168.

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