Advertisement

NGCE – Network Graphs for Computer Epidemiologists

  • Vasileios Vlachos
  • Vassiliki Vouzi
  • Damianos Chatziantoniou
  • Diomidis Spinellis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3746)

Abstract

Graphs are useful data structures capable of efficiently representing a variety of technological and social networks. They are therefore utilized in simulation-based studies of new algorithms and protocols. Inspired by the popular tgff (Task Graphs For Free) toolkit, which creates task graphs for embedded systems, we present the ngce, an easy to use graph generator that produces structures for the study of the propagation of viral agents in complex computer networks.

Designated track: Computer Security

Keywords

Network graphs Computer epidemiology Malicious software 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Paxson, V., Floyd, S.: Why we don’t know how to simulate the internet. In: Proceedings of the Winder Communication Conference (1997)Google Scholar
  2. 2.
    Palmer, C., Steffan, J.: Generating networks topologies that obey power laws. In: Proceedings of the GLOBECOM 2003, San Francisco, USA (2000)Google Scholar
  3. 3.
    Tangmunarunkit, H., Govindan, R., Jamin, S., Shenker, S., Willinger, W.: Network topology generators: Degree-based vs. structural. In: Proceedings of ACM SIGCOMM 2002, Pittsburgh, Pennsylvania, USA, pp. 147–159 (2002)Google Scholar
  4. 4.
    Dick, R., Rhodes, D., Wolf, W.: Tgff: Task graphs for free. In: Proceedings of International Workshop on Hardware/Software Codesign (Codes/CACHE 1997), pp. 97–101 (1998)Google Scholar
  5. 5.
    Berk, V., Bakos, G., Morris, R.: Designing a framework for active worm detection on global networks. In: Proceedings of the IEEE International Workshop on Information Assurance, Darmstad, Germany (2003)Google Scholar
  6. 6.
    Kephart, J., Chess, D., White, S.: Computers and epidemiology. IEEE Spectrum 30 (1993)Google Scholar
  7. 7.
    Kephart, J., White, S.: Measuring and modeling computer virus prevalence. In: Proceedings of the 1999 IEEE Computer Society Symposium on Research in Security and Privacy, Oakland, California, pp. 2–14 (1999)Google Scholar
  8. 8.
    Kephart, J.: How topology affects population dynamics. In: Proceedings of Artificial Life 3, New, Mexico,USA (1992)Google Scholar
  9. 9.
    Chen, Z., Gao, L., Kwiat, K.: Modeling the spread of active worms. In: Proceedings of the IEEE Infocom, San Francisco, USA (2003)Google Scholar
  10. 10.
    Zou, C., Gong, W., Towsley, D.: Code red worm propagation modeling and analysis. In: Proceedings of the 9th ACM Conference on Computer and Communication Security (CCS), Washington DC, USA (2002)Google Scholar
  11. 11.
    Staniford, S., Paxson, V., Weaver, N.: How to 0wn the internet in your spare time. In: Proceedings of the 11th USENIX Security Symposium (2002)Google Scholar
  12. 12.
    Staniford, S.: Containment of scanning worms in enterprise networks. Journal of Computer Security (2003)Google Scholar
  13. 13.
    Scandariato, R., Knight, J.: An automated defense system to counter internet worms. Submitted to SRPS 2004, 23rd Symposium on Reliable Distributed Systems, Florianapolis, Brazil (2004)Google Scholar
  14. 14.
    Zou, C., Gao, L., Gong, W., Towsley, D.: Monitoring and early warning for internet worms. In: Proceedings of the 10th ACM Conference on Computer and Communication Security, Washington DC, USA (2003)Google Scholar
  15. 15.
    Leveille, J.: Epidemic spreading in technological networks. Hpl-2002-287, School of Cognitive and Computing Sciences, University of Sussex at Brighton, Bristol (2002)Google Scholar
  16. 16.
    Barabási, A., Albert, R., Jeong, H.: Scale-free characteristics of random networks: the topology of the world-wide web. Physica A 281 (1999)Google Scholar
  17. 17.
    Zou, C., Towsley, D., Gong, W.: Email virus propagation modeling and analysis. Technical report, University of Massachusetts Amherst, ECE TR-03-CSE-04 (2003)Google Scholar
  18. 18.
    Barabási, A., Albert, R., Jeong, H.: Mean-field theory for scale-free random networks. Physica A 272, 173–187 (1999)CrossRefGoogle Scholar
  19. 19.
    Virtanen, S.: Properties of nonuniform random graph models. Research Report HUT-TCS-A77, Helsinki University of Technology, Laboratory for Theoretical Computer Science (2003)Google Scholar
  20. 20.
    Barabási, A., Bonabeau, E.: Scale-free networks. Scientific American, 60–69 (2003)Google Scholar
  21. 21.
    Kanovsky, I., Mazor, S.: Models of web-like graphs: Integrated approach. In: Proceedings of the 7th World Multiconference on Systematics, Cybernetics and Informatics (SCI 2003), Orlando, Florida, USA, pp. 278–283 (2003)Google Scholar
  22. 22.
    Adamic, L., Huberman, B.: Power-law distribution of the world wide web. Science 287 (2000)Google Scholar
  23. 23.
    Bornholdt, S., Ebel, H.: World wide web scaling exponent from simon’s 1955 model. Physical Review E 64, 0345104 (R)–1– 0345104–4 (2001)Google Scholar
  24. 24.
    Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationships of the internet topology. In: Proceedings of ACM SIGCOMM, Cambridge, MA, USA, pp. 251–262 (1999)Google Scholar
  25. 25.
    Medina, A., Matta, I., Byers, J.: On the origin of power laws in internet topologies. ACM Computer Communication Review 30, 160–163 (2000)CrossRefGoogle Scholar
  26. 26.
    Ebel, H., Mielsch, L., Bornloldt, S.: Scale-free topology of e-mail networks. Physical Review E 66 (2002)Google Scholar
  27. 27.
    Wang, C., Knight, J., Elder, M.: On computer viral infection and the effect of immunization. In: Proceedings of the 16th Annual Computer Security Applications Conference (ACSAC),New Orleans, Louisiana, USA, pp. 246–256 (2000)Google Scholar
  28. 28.
    Pastor-Satorras, R., Vespignani, A.: Epidemic spreading in scale-free networks. Physical Review Letters 86, 3200–3203 (2001)CrossRefGoogle Scholar
  29. 29.
    Cohen, R., Erez, K., ben Avraham, D., Havlin, S.: Resilience of the internet to random breakdowns. Physical Review Letters 85, 4626 (2000)CrossRefGoogle Scholar
  30. 30.
    Barabási, A., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)CrossRefMathSciNetGoogle Scholar
  31. 31.
    Yook, S., Jeong, H., Barabási, A.: Modeling the internet’s large scale topology. In: PNAS Proceedings of the National Academy of Science, pp. 13382–13386 (2002)Google Scholar
  32. 32.
    Zegura, E., Calvert, K., Donahoo, M.: A quantitative comparison of graph-based models for internet topology. IEEE/ACM Transactions On Networking 5, 770–783 (1997)CrossRefGoogle Scholar
  33. 33.
    Zegura, E., Calvert, K., Bhattacharjee, S.: How to model an internetwork. In: Proceedings IEEE Infocom, San Francisco, CA, vol. 2, pp. 594–602 (1996)Google Scholar
  34. 34.
    Eagan, K.C.A., Merugu, S., Namjoshi, A., Stasko, J., Zegura, E.: Extending and enhancing gt-itm. In: Proceedings of the ACM SIGCOMM 2003 Workshops, Karlsruhe, Germany, pp. 23–27 (2003)Google Scholar
  35. 35.
    Winick, J., Jamin, S.: Inet-3.0: Internet topology generator. Technical Report UM-CSE-TR-456-02, EECS, University of Michigan (2002)Google Scholar
  36. 36.
    Medina, A., Lakhina, A., Matta, I., Byers, J.: Brite: An approach to universal topology generation. In: Proceedings of the International Workshop on Modeling, Analysis and Simulation of Computer and Telecommunications Systems, MASCOTS 2001, Cincinnati, Ohio, USA (2001)Google Scholar
  37. 37.
    Medina, A., Lakhina, A., Matta, I., Byers, J.: Brite: Universal topology generation from a user’s perspective. In: Proceedings of the International Workshop on Modeling, Analysis and Simulation of Computer and Telecommunications Systems (MASCOTS 2001), Cincinnati, OH (2001)Google Scholar
  38. 38.
    Dreier, D.: Manual of Operation: Barabási Graph Generator v1.0. University of California Riverside, Department of Computer Science (2002)Google Scholar
  39. 39.
    Berners-Lee, T., Fielding, R., Irvine, U., Masinter, L.: rfc2396 (October 2004), http://www.ietf.org/rfc/rfc2396.txt (1998)
  40. 40.
    Batagelj, V., Mrvar, A.: Program for Analysis and Visualization of Large Networks, Ljubljana, Slovenia (2004)Google Scholar
  41. 41.
    Li, L.: Java Data Structures and Programming. Springer, Berlin (1998)zbMATHGoogle Scholar
  42. 42.
    Albert, R., Barabási, A.: Statistical mechanics of complex networks. Reviews of Modern Physics 74, 47–97 (2002)CrossRefMathSciNetzbMATHGoogle Scholar
  43. 43.
    Daley, D., Gani, J.: Epidemic Modelling. Cambridge University Press, Cambridge (1999)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Vasileios Vlachos
    • 1
  • Vassiliki Vouzi
    • 1
  • Damianos Chatziantoniou
    • 1
  • Diomidis Spinellis
    • 1
  1. 1.Department of Management Science and TechnologyAthens University of Economics and Business (AUEB)AthensGreece

Personalised recommendations