An Anti-worm with Balanced Tree Based Spreading Strategy

  • Yi-xuan Liu
  • Xiao-chun Yun
  • Bai-ling Wang
  • Hai-bin Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3930)


The traditional prevention methods of anti-virus software cannot provide a safe network against malicious worms. In this paper, we research an anti-worm mechanism that actively distributes the anti-worm code to the authorized hosts. We propose a Balanced Tree based Propagation strategy(BTP) for an anti-worm strategy with a mathematic model. By varying the model parameters, the impacts can be studied. Some simulation results show us that the new strategy is effective and feasible.


Infected Host Epidemic Model Worm Propagation Antivirus Software Internet Worm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chen, T.M.: Trends in Viruses and Worms. The Internet Protocol Journal 6(3) (September 2003)Google Scholar
  2. 2.
    Levy, E.: Approaching Zero. IEEE Security & Privacy Magazine 2(4), 65–66 (2004)CrossRefGoogle Scholar
  3. 3.
    Moore, D., Paxson, V., Shannon, C.: Inside the Slammer Worm. In: IEEE Security and Privacy (2003)Google Scholar
  4. 4.
    Castaneda, F., Sezer, E.C., Xu, J.: WORM vs. WORM: Preliminary Study of an Active Counter-Attack Mechanism. In: Proceedings of ACM Workshop on Rapid Malcode (WORM 2004) (October 2004)Google Scholar
  5. 5.
    Bai-ling, W., Bin-xing, F., Xiao-chun, Y.: The Propagation Model and Analysis of Worms Together with Anti-Worms. WSEAS Transactions on Information Science and Applications 1(4) ( October 2004)Google Scholar
  6. 6.
    Wei-Ping, W., Si-Han, Q., et al.: Research and Development of Internet Worms. Journal of Software 15(8) (2004)Google Scholar
  7. 7.
    Zou, C.C., Gong, W., Towsley, D.: Code Red worm propagation modeling and analysis. In: Proc. of the 9th ACM Symp. on Computer and Communication Security, Washington (2002)Google Scholar
  8. 8.
    Kephart, J.O., White, S.R.: Measuring and Modeling Computer Virus Prevalence. In: Proceedings of the IEEE Symposium on Security and Privacy (1993)Google Scholar
  9. 9.
    Kephart, J.O., Chess, D.M., White, S.R.: Computers and Epidemiology. IEEE Spectrum (1993)Google Scholar
  10. 10.
    Zou, C.C., Towsley, D., Gong, W.: On the Performance of Internet Worm Scanning Strategies. Technical Report: TR-03-CSE-07Google Scholar
  11. 11.
    Staniford, S., et al.: The Top Speed of Flash Worms. In: ACM Workshop on Rapid Malcode (WORM 2004), George Mason University, Fairfax, Virginia, USA (2004)Google Scholar
  12. 12.
    Arce, I., Levy, E.: An Analysis of the Slapper Worm. IEEE Security & Privacy (2003)Google Scholar
  13. 13.
    Wu, J., Vangala, S., Gao, L.: An Effective Architecture and Algorithm for Detecting Worms with Various Scan Techniques. In: Network and Distributed System Security Symposium (2004)Google Scholar
  14. 14.
    PDNS - Parallel/Distributed NS,

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yi-xuan Liu
    • 1
  • Xiao-chun Yun
    • 1
  • Bai-ling Wang
    • 1
  • Hai-bin Sun
    • 2
  1. 1.Harbin Institute of Technology 
  2. 2.The Chinese University of Hong Kong 

Personalised recommendations