Advertisement

Performance Analysis of Single and Multi-objective Approaches for the Critical Node Detection Problem

  • Luca FaramondiEmail author
  • Gabriele Oliva
  • Roberto Setola
  • Federica Pascucci
  • Annunziata Esposito Amideo
  • Maria Paola Scaparra
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 217)

Abstract

Critical infrastructures are network-based systems which are prone to various types of threats (e.g., terroristic or cyber-attacks). Therefore, it is paramount to devise modelling frameworks to assess their ability to withstand external disruptions and to develop protection strategies aimed at improving their robustness and security. In this paper, we compare six modelling approaches for identifying the most critical nodes in infrastructure networks. Three are well-established approaches in the literature, while three are recently proposed frameworks. All the approaches take the perspective of an attacker whose ultimate goal is to inflict maximum damage to a network with minimal effort. Specifically, they assume that a saboteur must decide which nodes to disable so as to disrupt network connectivity as much as possible. The formulations differ in terms of the attacker objectives and connectivity metrics (e.g., trade-off between inflicted damage and attack cost, pair-wise connectivity, size and number of disconnected partitions). We apply the six formulations to the IEEE24 and IEEE118 Power Systems and conduct a comparative analysis of the solutions obtained with different parameters settings. Finally, we use frequency analysis to determine the most critical nodes with respect to different attack strategies.

Keywords

Critical infrastructures Network vulnerability Critical node detection problem 

References

  1. 1.
    Réka, A., Hawoong, J., Barabási, A.: Error and attack tolerance of complex networks. Nature 406(6794), 378–382 (2000)CrossRefGoogle Scholar
  2. 2.
    Holme, P., Kim, B.J., Yoon, C.N., Han, S.K.: Attack vulnerability of complex networks. Phys. Rev. E 65(5), 056109 (2002)CrossRefGoogle Scholar
  3. 3.
    Wu, J., Deng, H.Z., Tan, Y.J., Zhu, D.Z.: Vulnerability of complex networks under intentional attack with incomplete information. J. Phys. A: Math. Theor. 40(11), 2665 (2007)CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    Huang, X., Gao, J., Buldyrev, S.V., Havlin, S., Stanley, H.E.: Robustness of interdependent networks under targeted attack. Phys. Rev. E 83, 065101 (2011)CrossRefGoogle Scholar
  5. 5.
    Shao, S., Huang, X., Stanley, H.E., Havlin, S.: Percolation of localized attack on complex networks. New J. Phys. 17(2), 023049 (2015)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Murray, A.T.: An overview of network vulnerability modeling approaches. GeoJournal 78, 209–221 (2013)CrossRefGoogle Scholar
  7. 7.
    Arulselvan, A., Commander, C.W., Elefteriadou, L., Pardalos, P.M.: Detecting critical nodes in sparse graphs. Comput. Oper. Res. 36(7), 2193–2200 (2009)CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Shen, Y., Nguyen, N.P., Xuan, Y., Thai, M.T.: On the discovery of critical links and nodes for assessing network vulnerability. IEEE/ACM Trans. Netw. (TON) 21(3), 963–973 (2013)CrossRefGoogle Scholar
  9. 9.
    Di Summa, M., Grosso, A., Locatelli, M.: Branch and cut algorithms for detecting critical nodes in undirected graphs. Comput. Optim. Appl. 53, 649–680 (2013)zbMATHMathSciNetGoogle Scholar
  10. 10.
    Sun, F., Shayman, M.A.: On pairwise connectivity of wireless multihop networks. Int. J. Secur. Netw. 2(1–2), 37–49 (2007)CrossRefGoogle Scholar
  11. 11.
    Dinh, T.N., Xuan, Y., Thai, M.T., Park, E.K., Znati, T.: On approximation of new optimization methods for assessing network vulnerability. INFOCOM 2010, 1–9 (2010)Google Scholar
  12. 12.
    Pullan, W.: Heuristic identification of critical nodes in sparse real-world graphs. J. Heuristics 21(5), 577–598 (2015)CrossRefGoogle Scholar
  13. 13.
    Lalou, M., Tahraoui, M.A., Kheddouci, H.: Component-cardinality-constrained critical node problem in graphs. Discret. Appl. Math. 210, 150–163 (2015)CrossRefzbMATHMathSciNetGoogle Scholar
  14. 14.
    Ventresca M., Harrison K.R., Ombuki-Berman B.M.: An experimental evaluation of multi-objective evolutionary algorithms for detecting critical nodes in complex networks. In: Mora, A., Squillero, G. (eds.) Applications of Evolutionary Computation. EvoApplications 2015. Lecture Notes in Computer Science, vol. 9028 (2015)Google Scholar
  15. 15.
    Faramondi, L., Oliva, G., Pascucci, F., Panzieri, S., Setola, R.: Critical node detection based on attacker preferences. In: 24th Mediterranean Conference on Control and Automation (MED), pp. 773–778 (2016)Google Scholar
  16. 16.
    Faramondi, L.: Critical node detection problem: vulnerabilities and robustness metrics. Doctoral dissertation (2017)Google Scholar
  17. 17.
    Arulselvan, A., Commander, C.W., Shylo, O., Pardalos, P.M.: Cardinality-constrained critical node detection problem. In: Performance Models and Risk Management in Communications Systems, pp. 79–91 (2011)Google Scholar
  18. 18.
    Dam, Q.B., Meliopoulos, A.S., Heydt, G.T., Bose, A.: A breaker-oriented, three-phase IEEE 24-substation test system. IEEE Trans. Power Syst. 25(1), 59–67 (2010)CrossRefGoogle Scholar
  19. 19.
    Christie, R.D.: IEEE power systems test case archive, http://ee.washington.edu/research/pstca, (1999). Accessed 8 August 2017

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Luca Faramondi
    • 1
    Email author
  • Gabriele Oliva
    • 1
  • Roberto Setola
    • 1
  • Federica Pascucci
    • 2
  • Annunziata Esposito Amideo
    • 3
  • Maria Paola Scaparra
    • 3
  1. 1.Universitá Campus Bio-MedicoRomeItaly
  2. 2.Department of EngineeringUniversity Roma TreRomeItaly
  3. 3.Kent Business SchoolUniversity of KentCanterbury, KentUK

Personalised recommendations