Advertisement

Identification of Vulnerabilities in Networked Systems

  • Luca FaramondiEmail author
  • Roberto Setola
Chapter
Part of the Advanced Sciences and Technologies for Security Applications book series (ASTSA)

Abstract

In last decades, thanks to the large diffusion of Information and communications technologies, the cooperation of distributed systems has been facilitated with the aim to provide new services. One of the common aspect of this kind of systems is the presence of a network able to ensure the connectivity among the elements of the network. The connectivity is a fundamental prerequisite also in the context of critical infrastructures (CIs), which are defined as a specific kind of infrastructures able to provide the essential services that underpin the society and serve as the backbone of our nation’s economy, security, and health (i.e. transportation systems, gas and water distribution systems, financial services, etc). Due to their relevance, the identification of vulnerabilities in this kind of systems is a mandatory task in order to design adequate and effective defense strategies. To this end, in this chapter some of the most common methods for networks vulnerabilities identification are illustrated and compared in order to stress common aspects and differences.

Keywords

Critical nodes Network vulnerabilities Optimization approach 

References

  1. 1.
    Arulselvan A, Commander CW, Elefteriadou L, Pardalos PM (2009) Detecting critical nodes in sparse graphs. Comput Oper Res 36(7):2193–2200MathSciNetCrossRefGoogle Scholar
  2. 2.
    Berezin Y, Bashan A, Danziger MM, Li D, Havlin S (2015) Localized attacks on spatially embedded networks with dependencies. Sci Rep 5:8934CrossRefGoogle Scholar
  3. 3.
    Bonacich P (2007) Some unique properties of eigenvector centrality. Soc Netw 29(4):555–564CrossRefGoogle Scholar
  4. 4.
    Brandes U (2001) A faster algorithm for betweenness centrality. J Math Soc 25(2):163–177 ISO 690CrossRefGoogle Scholar
  5. 5.
    Censor Y (1977) Pareto optimality in multiobjective problems. Appl Math Optim 4:41–59MathSciNetCrossRefGoogle Scholar
  6. 6.
    Dinh TN, Xuan Y, Thai MT, Park EK, Znati T (2010) On approximation of new optimization methods for assessing network vulnerability. In: INFOCOM, 2010 Proceedings IEEE, San Diego, pp 1–9. IEEEGoogle Scholar
  7. 7.
    Esposito JM, Dunbar TW (2006) Maintaining wireless connectivity constraints for swarms in the presence of obstacles. In: Proceedings 2006 IEEE International Conference on Robotics and Automation, ICRA’06, Orlando, pp 946–951. IEEEGoogle Scholar
  8. 8.
    Faramondi L, Oliva G, Pascucci F, Panzieri S, Setola R (2016) Critical node detection based on attacker preferences. In: 2016 24th Mediterranean Conference on Control and Automation (MED), Athens, pp 773–778. IEEEGoogle Scholar
  9. 9.
    Faramondi L, Setola R, Panzieri S, Pascucci F, Oliva G (2017) Finding critical nodes in infrastructure networks. Int J Crit Infrastruct Prot 20:3–15CrossRefGoogle Scholar
  10. 10.
    Faramondi L, Oliva G, Panzieri S, Pascucci F, Schlueter M, Munetomo M, Setola R (2018) Network structural vulnerability: a multiobjective attacker perspective. IEEE Trans Syst Man Cybern SystGoogle Scholar
  11. 11.
    Holme P, Kim BJ, Yoon CN, Han SK (2002) Attack vulnerability of complex networks. Phys Rev E 65(5):056109CrossRefGoogle Scholar
  12. 12.
    Huang X, Gao J, Buldyrev SV, Havlin S, Stanley HE (2011) Robustness of interdependent networks under targeted attack. Phys Rev E 83:065101CrossRefGoogle Scholar
  13. 13.
    ICS-CERT USDHS (2014) ICS-monitor incident response activity. National Cybersecurity and Communications Integration CenterGoogle Scholar
  14. 14.
    Lalou M, Tahraoui MA, Kheddouci H (2016) Component-cardinality-constrained critical node problem in graphs. Discret Appl Math 210:150–163MathSciNetCrossRefGoogle Scholar
  15. 15.
    Lalou M, Tahraoui MA, Kheddouci H (2018) The critical node detection problem in networks: a survey. Comput Sci Rev 28:92–117MathSciNetCrossRefGoogle Scholar
  16. 16.
    Louzada VHP, Daolio F, Herrmann HJ, Tomassini M (2015) Generating robust and efficient networks under targeted attacks. In: Dariusz K, Damien F, Bogdan G (eds) Propagation phenomena in real world networks. Springer, Cham/Heidelberg/New York/Dordrecht/London, pp 215–224Google Scholar
  17. 17.
    Lu ZM, Li XF (2016) Attack vulnerability of network controllability. PloS One 11(9):e0162289CrossRefGoogle Scholar
  18. 18.
    Pullan W (2015) Heuristic identification of critical nodes in sparse real-world graphs. J Heuristics 21(5):577–598CrossRefGoogle Scholar
  19. 19.
    Réka A, Hawoong J, Barabási A (2000) Error and attack tolerance of complex networks. Nature 406(6794):378–382CrossRefGoogle Scholar
  20. 20.
    Schrijver A (1998) Theory of linear and integer programming. Wiley, Chichester/New YorkzbMATHGoogle Scholar
  21. 21.
    Shao S, Huang X, Stanley HE, Havlin S (2015) Percolation of localized attack on complex networks. New J Phys 17(2):023049MathSciNetCrossRefGoogle Scholar
  22. 22.
    Shen Y, Nguyen NP, Xuan Y, Thai MT (2013) On the discovery of critical links and nodes for assessing network vulnerability. IEEE/ACM Trans Netw (TON) 21(3):963–973CrossRefGoogle Scholar
  23. 23.
    Sun F, Shayman MA (2007) On pairwise connectivity of wireless multihop networks. Int J Secur Netw 2(1–2):37–49CrossRefGoogle Scholar
  24. 24.
    Ventresca M, Harrison KR, Ombuki-Berman BM (2015) An experimental evaluation of multi-objective evolutionary algorithms for detecting critical nodes in complex networks. In: European Conference on the Applications of Evolutionary Computation, Copenhagen, pp 164–176. SpringerGoogle Scholar
  25. 25.
    Wu J, Deng HZ, Tan YJ, Zhu DZ (2007) Vulnerability of complex networks under intentional attack with incomplete information. J Phys A Math Theor 40(11):2665MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Campus Bio-Medico UniversityRomeItaly

Personalised recommendations