Knowledge and Information Systems

, Volume 27, Issue 2, pp 253–279 | Cite as

Network immunization and virus propagation in email networks: experimental evaluation and analysis

  • Chao Gao
  • Jiming LiuEmail author
  • Ning Zhong
Regular Paper


Network immunization strategies have emerged as possible solutions to the challenges of virus propagation. In this paper, an existing interactive model is introduced and then improved in order to better characterize the way a virus spreads in email networks with different topologies. The model is used to demonstrate the effects of a number of key factors, notably nodes’ degree and betweenness. Experiments are then performed to examine how the structure of a network and human dynamics affects virus propagation. The experimental results have revealed that a virus spreads in two distinct phases and shown that the most efficient immunization strategy is the node-betweenness strategy. Moreover, those results have also explained why old virus can survive in networks nowadays from the aspects of human dynamics.


Immunization strategies Virus propagation Human dynamics Email networks Enron 


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  1. 1.
    Bailey NTJ (1975) The mathematical theory of infectious diseases and its applications. Hafner Press, New YorkzbMATHGoogle Scholar
  2. 2.
    Barabasi AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439): 509–512CrossRefMathSciNetGoogle Scholar
  3. 3.
    Barabasi AL (2005) The origin of bursts and heavy tails in human dynamics. Nature 435(7039): 207–211CrossRefGoogle Scholar
  4. 4.
    Bar-Yossef Z, Guy I, Lempel R, Maarek YS, Soroka V (2008) Cluster ranking with an application to mining mailbox networks. Knowl Inf Syst 14: 101–139CrossRefGoogle Scholar
  5. 5.
    Boots M, Sasaki A, Ben-Averaham D (1999) ‘Small worlds’ and the evolution of virulence: infection occurs locally and at a distance. Proc R Soc Lond B Biol Sci 266(1432): 1933–1938CrossRefGoogle Scholar
  6. 6.
    Bu T, Towsley D (2002) On distinguishing between internet power law topology generators. In: Lee D, Orda A (eds) Proceedings of the twenty first annual joint conference of the IEEE computer and communications societies (INFOCOM’02). IEEE Press, New York, pp 638–647Google Scholar
  7. 7.
    Clauset A, Shalizi CR, Newman MEJ (2009) Power-law distribution in empirical data. SIAM Rev 51(4): 661–703CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Cohen R, Havlin S, Ben-Averaham D (2003) Efficient immunization strategies for computer networks and populations. Phys Rev Lett 91(24): 247901CrossRefGoogle Scholar
  9. 9.
    Dezso Z, Barabasi AL (2002) Halting viruses in scale-free networks. Phys Rev E 65(5): 055103CrossRefGoogle Scholar
  10. 10.
    Dezso Z, Almaas E, Lukacs A, Racz B, Szakadat I, Barabasi AL (2006) Dynamics of information access on the web. Phys Rev E 73(6): 066132CrossRefGoogle Scholar
  11. 11.
    Earn DJD, Rohani P, Bolker BM, Grenfell BT (2000) A simple model for complex dynamical transitions in epidemics. Science 287(5453): 667–670CrossRefGoogle Scholar
  12. 12.
    Echenique P, Gomez-Gardenes J, Moreno Y, Vazquez A (2005) Distance-d covering problem in scale-free networks with degree correlation. Phys Rev E 71(3): 035102CrossRefGoogle Scholar
  13. 13.
    Eckmann JP, Moses E, Sergi D (2004) Entropy of dialogues creates coherent structure in email traffic. Proc Natl Acad Sci U S A 101(40): 14333–14337CrossRefzbMATHMathSciNetGoogle Scholar
  14. 14.
    Eguiluz VM, Klemm K (2002) Epidemic threshold in structured scale-free networks. Phys Rev Lett 89(10): 108701CrossRefGoogle Scholar
  15. 15.
    Faloutsos M, Faloutsos P, Faloutsos C (1999) On power-law relationships of the internet topology. ACM SIGCOMM Comput Commun Rev 29(4): 251–262CrossRefGoogle Scholar
  16. 16.
    Gallos LK, Liljeros F, Argyrakis P, Bunde A, Havlin S (2007) Improving immunization strategies. Phys Rev E 75(4): 045104CrossRefGoogle Scholar
  17. 17.
    Gomez-Gardenes J, Echenique P, Moreno Y (2002) Immunization of real complex communication networks. Eur Phys J B 49(2): 259–264CrossRefGoogle Scholar
  18. 18.
    Guimera R, Danon L, Diaz-Guilera A, Giralt F, Arenas A (2004) Self-similar community structure in a network of human interactions. Phys Rev E 68(6): 065103CrossRefGoogle Scholar
  19. 19.
    Holme P, Kim BJ, Yoon CN, Han SK (2002) Attack vulnerability of complex networks. Phys Rev E 65(5): 056109CrossRefGoogle Scholar
  20. 20.
    Huang XL, Zou FT, Ma FY (2007) Targeted local immunization in scale-free peer-to-peer networks. J Comput Sci Technol 22(3): 457–468CrossRefGoogle Scholar
  21. 21.
    Jeong H, Tombor B, Albert R, OItvai ZN, Barabasi AL (2000) The large scale organization of metabolic networks. Nature 407(6804): 651–654CrossRefGoogle Scholar
  22. 22.
    Johansen A (2004) Probing human response times. Physica A 338(1–2): 286–291CrossRefGoogle Scholar
  23. 23.
    Lahiri M, Berger-Wolf TY (2009) Periodic subgraph mining in dynamic networks. Knowledge and information systems. doi: 10.1007/s10115-009-0253-8
  24. 24.
    Liu JM, Gao C, Zhong N (2010) Autonomy-oriented search in dynamic community networks: a case study in decentralized network immunization. Fundam Informaticae 99(2): 207–226MathSciNetGoogle Scholar
  25. 25.
    Liu JM, Zhang SW, Yang J (2004) Characterizing web usage regularities with information foraging agents. IEEE Trans Knowl Data Eng 16(5): 566–584CrossRefGoogle Scholar
  26. 26.
    Lloyd AL, May RM (2001) How viruses spread among computers and people. Science 292(5520): 1316–1317CrossRefGoogle Scholar
  27. 27.
    Malmgren RD, Stouffer DB, Campanharo ASLO, Amaral LAN (2009) On universality in human correspondence activity. Science 325(5948): 1696–1700CrossRefGoogle Scholar
  28. 28.
    May SR (2000) Enhanced: simple rules with complex dynamics. Science 287(5453): 601–602CrossRefGoogle Scholar
  29. 29.
    Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U (2002) Network motifs simple building blocks of complex networks. Science 298(5594): 824–827CrossRefGoogle Scholar
  30. 30.
    Moore C, Newman MEJ (2000) Epidemics and percolation in small-world network. Phys Rev E 61(5): 5678–5682CrossRefGoogle Scholar
  31. 31.
    Moore D, Shannon C, Brown J (2002) Code-red: a case study on the spread and victims of an internet worm. Proceedings of the 2nd ACM SIGCOMM Workshop on Internet Measurement(IMW’02), Marseille, France, pp 273–284Google Scholar
  32. 32.
    Newman MEJ (2001) The structure of scientific collaboration networks. Proc Natl Acad Sci U S A 98(2): 404–409CrossRefzbMATHGoogle Scholar
  33. 33.
    Newman MEJ (2002) The spread of epidemic disease on networks. Phys Rev E 66(1): 016128CrossRefMathSciNetGoogle Scholar
  34. 34.
    Newman MEJ (2003) The structure and function of complex networks. SIAM Rev 45(2): 167–256CrossRefzbMATHMathSciNetGoogle Scholar
  35. 35.
    Newman MEJ, Forrest S, Balthrop J (2002) Email networks and the spread of computer viruses. Phys Rev E 66(3): 035101CrossRefGoogle Scholar
  36. 36.
    Narasimhamurthy A, Greene D, Hurley N, Cunningham P (2009) Partitioning large networks without breaking communities. Knowl Inf Syst. doi: 10.1007/s10115-009-0251-x
  37. 37.
    Pastor-Satorras R, Vespignani A (2001a) Epidemic spreading in scale-free networks. Phys Rev Lett 86(14): 3200–3203CrossRefGoogle Scholar
  38. 38.
    Pastor-Satorras R, Vespignani A (2001b) Epidemic dynamics and endemic states in complex networks. Phys Rev E 63(6): 066117CrossRefGoogle Scholar
  39. 39.
    Pastor-Satorras R, Vespignani A (2002) Immunization of complex networks. Phys Rev E 65(3): 036104CrossRefGoogle Scholar
  40. 40.
    Serazzi G, Zanero S (2004) Computer virus propagation models. In: Calzarossa M, Gelenbe E (eds) Performance tools and applications to networked systems, revised tutorial lectures, LNCS 2965. Springer, Heidelberg, pp 26–50CrossRefGoogle Scholar
  41. 41.
    Shetty J, Adibi J (2004) The Enron email dataset database schema and brief statistical report. Technical report, Information Sciences InstituteGoogle Scholar
  42. 42.
    Strogatz SH (2001) Exploring complex networks. Nature 410(6825): 268–276CrossRefGoogle Scholar
  43. 43.
    Vazquez A, Oliveira JG, Dezso Z, Goh KI, Kondor I, Barabasi AL (2006) Modeling bursts and heavy tails in human dynamics. Phys Rev E 73(3): 036127CrossRefGoogle Scholar
  44. 44.
    Vazquez A, Racz B, Lukacs A, Barabasi AL (2007) Impact of non-poissonian activity patterns on spreading process. Phys Rev Lett 98(15): 158702CrossRefGoogle Scholar
  45. 45.
    Vespignani A (2009) Predicting the behavior of techno-social systems. Science 325(5939): 425–428CrossRefMathSciNetGoogle Scholar
  46. 46.
    Wang D, Tse QCK, Zhou Y (2009) A decentralized search engine for dynamic web communities. Knowl Inf Syst. doi: 10.1007/s10115-009-0270-7
  47. 47.
    Watts DJ (2007) A twenty-first century science. Nature 445(7127): 489CrossRefGoogle Scholar
  48. 48.
    Whalley I, Arnold B, Chess D, Morar J, Segal A, Swimmer M (2000) An environment for controlled worm replication and analysis. Virus Bulletin 1–20Google Scholar
  49. 49.
    Zou CC, Towsley D, Gong W (2007) Modeling and simulation study of the propagation and defense of internet e-mail worms. IEEE Trans Dependable Secur Comput 4(2): 105–118CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  1. 1.International WIC InstituteBeijing University of TechnologyBeijingChina
  2. 2.Beijing Key Laboratory of Multimedia and Intelligent SoftwareBeijingChina
  3. 3.Department of Computer ScienceHong Kong Baptist UniversityKowloon TongHong Kong
  4. 4.Department of Life Science and InformaticsMaebashi Institute of TechnologyMaebashiJapan

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