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K-core-preferred Attack to the Internet: Is It More Malicious Than Degree Attack?

  • Jichang Zhao
  • Junjie Wu
  • Mingming Chen
  • Zhiwen Fang
  • Ke Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7923)

Abstract

K-core (k-shell) index is an interesting measure that describes the core and fringe nodes in a complex network. Recent studies have revealed that some high k-core value nodes may play a vital role in information diffusion. As a result, one may expect that attacking high k-core nodes preferentially can collapse the Internet easily. To our surprise, however, the experiments on two Internet AS-level topologies show that: Although a k-core-preferred attack is feasible in reality, it turns out to be less effective than a classic degree-preferred attack. Indeed, as indicated by the measure: normalized susceptibility, we need to remove 2% to 3% more nodes in a k-core-preferred attack to make the network collapsed. Further investigation on the nodes in a same shell discloses that these nodes often have degrees varied drastically, among which there are nodes with high k-core values but low degrees. These nodes cannot contribute many link deletions in an early stage of a k-core-preferred attack, and therefore make it less malicious than a degree-preferred attack.

Keywords

Robustness K-core AS-level Internet Malicious Attack 

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References

  1. 1.
    Albert, R., Jeong, H., Barabási, A.L.: Error and attack tolerance of complex networks. Nature 406(6794), 378–382 (2000)CrossRefGoogle Scholar
  2. 2.
    Boguñá, M., Papadopoulos, F., Krioukov, D.: Sustaining the internet with hyperbolic mapping. Nature Communications 1(62) (2010)Google Scholar
  3. 3.
    Brandes, U.: A faster algorithm for betweenness centrality. Journal of Mathematical Sociology 25, 163–177 (2001)MATHCrossRefGoogle Scholar
  4. 4.
    Butler, K., Farley, T., McDaniel, P., Rexford, J.: A survey of bgp security issues and solutions. Proceedings of the IEEE 98, 100–122 (2010)CrossRefGoogle Scholar
  5. 5.
    Carmi, S., Havlin, S., Kirkpatrick, S., Shavitt, Y., Shir, E.: A model of internet topology using k-shell decomposition. PNAS 104(27), 11150–11154 (2007)CrossRefGoogle Scholar
  6. 6.
    Cohen, R., Erez, K., Ben-Avraham, D., Havlin, S.: Resilience of the internet to random breakdowns. Phys. Rev. Lett. 85(21), 4626–4628 (2000)CrossRefGoogle Scholar
  7. 7.
    Cohen, R., Erez, K., Ben-Avraham, D., Havlin, S.: Breakdown of the internet under intentional attack. Phys. Rev. Lett. 86(16), 3682–3685 (2001)CrossRefGoogle Scholar
  8. 8.
    Cowie, J., Ogielski, A.T., Premore, B.J., Yuan, Y.: Internet worms and global routing instabilities. In: Proc. SPIE, vol. 4868 (2002)Google Scholar
  9. 9.
    Donnet, B., Friedman, T.: Internet topology discovery: a survey. IEEE Communications Surveys and Tutorials 9(4), 2–15 (2007)CrossRefGoogle Scholar
  10. 10.
    Dorogovtsev, S.N., Goltsev, A.V., Mendes, J.F.F.: k-core organization of complex networks. Phys. Rev. Lett. 96, 040601 (2006)CrossRefGoogle Scholar
  11. 11.
    Guillaume, J.L., Latapy, M., Magoni, D.: Relevance of massively distributed explorations of the internet topology: Qualitative results. Computer Networks 50, 3197–3224 (2006)MATHCrossRefGoogle Scholar
  12. 12.
    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
  13. 13.
    Huffaker, B., Plummer, D., Moore, D., Claffy, K.C.: Topology discovery by active probing. In: SAINT-W 2002, pp. 90–96 (2002)Google Scholar
  14. 14.
    Kitsak, M., Gallos, L.K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H.E., Makse, H.A.: Identification of influential spreaders in complex networks. Nature Physics 6, 888–893 (2010)CrossRefGoogle Scholar
  15. 15.
    Kumpula, J.M., Onnela, J.P., Saramäki, J., Kaski, K., Kertész, J.: Emergence of communities in weighted networks. Phys. Rev. Lett. 99(22), 228701 (2007)CrossRefGoogle Scholar
  16. 16.
    Latora, V., Marchiori, M.: Efficient behavior of small-world networks. Phys. Rev. Lett. 87(19), 198701 (2001)CrossRefGoogle Scholar
  17. 17.
    Liljenstam, M., Yuan, Y., Premore, B.J., Nicol, D.M.: A mixed abstraction level simulation model of large-scale internet worm infestations. In: MASCOTS 2002, pp. 109–116 (2002)Google Scholar
  18. 18.
    Schneider, C.M., Moreira, A.A., Andrade Jr., J.S., Havlin, S., Herrmann, H.J.: Mitigation of malicious attacks on networks. PNAS 108(10), 3838–3841 (2011)CrossRefGoogle Scholar
  19. 19.
    Seidman, S.B.: Network structure and minum degree. Social Networks 5, 269–287 (1983)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Shakkottai, S., Fomenkov, M., Koga, R., Krioukov, D., Claffy, K.: Evolution of the internet as-level ecosystem. European Physical Journal B 74, 271–278 (2006)Google Scholar
  21. 21.
    Zhang, G.Q., Zhang, G.Q., Yang, Q.F., Cheng, S.Q., Zhou, T.: Evolution of the internet and its cores. New J. Phys. 10(12), 123027 (2008)CrossRefGoogle Scholar
  22. 22.
    Zhang, J., Zhao, H., Xu, J., Liu, Z.: Characterizing and modeling the internet router-level topology - the hierarchical features and hir model. Comput. Commun. 33, 2001–2011 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jichang Zhao
    • 1
  • Junjie Wu
    • 2
  • Mingming Chen
    • 1
  • Zhiwen Fang
    • 1
  • Ke Xu
    • 1
  1. 1.State Key Laboratory of Software Development EnvironmentBeihang UniversityChina
  2. 2.Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operations, School of Economics & ManagementBeihang UniversityChina

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