skip to main content
10.1145/2746194.2746196acmotherconferencesArticle/Chapter ViewAbstractPublication PageshotsosConference Proceedingsconference-collections
research-article

Active cyber defense dynamics exhibiting rich phenomena

Published:21 April 2015Publication History

ABSTRACT

The Internet is a man-made complex system under constant attacks (e.g., Advanced Persistent Threats and malwares). It is therefore important to understand the phenomena that can be induced by the interaction between cyber attacks and cyber defenses. In this paper, we explore the rich phenomena that can be exhibited when the defender employs active defense to combat cyber attacks. To the best of our knowledge, this is the first study that shows that active cyber defense dynamics (or more generally, cybersecurity dynamics) can exhibit the bifurcation and chaos phenomena. This has profound implications for cyber security measurement and prediction: (i) it is infeasible (or even impossible) to accurately measure and predict cyber security under certain circumstances; (ii) the defender must manipulate the dynamics to avoid such unmanageable situations in real-life defense operations.

References

  1. D. Aitel. Nematodes -- beneficial worms. http://www.immunityinc.com/downloads/nematodes.pdf, Sept. 2005.Google ScholarGoogle Scholar
  2. F. Castaneda, E. Sezer, and J. Xu. Worm vs. worm: preliminary study of an active counter-attack mechanism. In Proceedings of ACM WORM'04, pages 83--93, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. D. Chakrabarti, Y. Wang, C. Wang, J. Leskovec, and C. Faloutsos. Epidemic thresholds in real networks. ACM Trans. Inf. Syst. Secur., 10(4): 1--26, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. G. Da, M. Xu, and S. Xu. A new approach to modeling and analyzing security of networked systems. In Proceedings of HotSoS'14, pages 6:1--6:12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. P. S. Dodds, K. D. Harris, and C. M. Danforth. Limited imitation contagion on random networks: Chaos, universality, and unpredictability. Phys. Rev. Lett., 110: 158701, Apr 2013.Google ScholarGoogle ScholarCross RefCross Ref
  6. A. Ganesh, L. Massoulie, and D. Towsley. The effect of network topology on the spread of epidemics. In Proceedings of IEEE Infocom 2005, 2005.Google ScholarGoogle ScholarCross RefCross Ref
  7. Y. Han, W. Lu, and S. Xu. Characterizing the power of moving target defense via cyber epidemic dynamics. In Proceedings of HotSoS'14, pages 10:1--10:12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. H. Hethcote. The mathematics of infectious diseases. SIAM Rev., 42(4): 599--653, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. J. Hofbauer and K. Sigmund. The theory of evolution and dynamical systems. Cambridge University Press, 1998.Google ScholarGoogle Scholar
  10. R. Horn and C. Johnson. Matrix Analysis. Cambridge University Press, 1985. Google ScholarGoogle ScholarCross RefCross Ref
  11. J. Kephart and S. White. Directed-graph epidemiological models of computer viruses. In IEEE Symposium on Security and Privacy, pages 343--361, 1991.Google ScholarGoogle ScholarCross RefCross Ref
  12. J. Kephart and S. White. Measuring and modeling computer virus prevalence. In IEEE Symposium on Security and Privacy, pages 2--15, 1993. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. W. Kermack and A. McKendrick. A contribution to the mathematical theory of epidemics. Proc. of Roy. Soc. Lond. A, 115: 700--721, 1927.Google ScholarGoogle ScholarCross RefCross Ref
  14. J. Kesan and C. Hayes. Mitigative counterstriking: Self-defense and deterrence in cyberspace. Harvard Journal of Law and Technology (forthcoming, available at SSRN: http://ssrn.com/abstract=1805163).Google ScholarGoogle Scholar
  15. P. L. Krapivsky. Kinetics of monomer-monomer surface catalytic reactions. Phys. Rev. A, 45: 1067--1072, Jan 1992.Google ScholarGoogle ScholarCross RefCross Ref
  16. H. Lin. Lifting the veil on cyber offense. IEEE Security & Privacy, 7(4): 15--21, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. W. Lu, S. Xu, and X. Yi. Optimizing active cyber defense dynamics. In Proceedings of GameSec'13, pages 206--225. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. W. Matthews. U.s. said to need stronger, active cyber defenses. http://www.defensenews.com/story.php?i=4824730, 1 Oct 2010.Google ScholarGoogle Scholar
  19. A. McKendrick. Applications of mathematics to medical problems. Proc. of Edin. Math. Soceity, 14: 98--130, 1926.Google ScholarGoogle Scholar
  20. A. Milanese, J. Sun, and T. Nishikawa. Approximating spectral impact of structural perturbations in large networks. Phys. Rev. E, 81: 046112, Apr 2010.Google ScholarGoogle ScholarCross RefCross Ref
  21. J. Morales, S. Xu, and R. Sandhu. Analyzing malware detection efficiency with multiple anti-malware programs. In Proceedings of 2012 ASE CyberSecurity'12.Google ScholarGoogle Scholar
  22. R. Naraine. 'friendly' welchia worm wreaking havoc. http://www.internetnews.com/ent-news/article.php/3065761/Friendly-Welchia-Worm-Wreaking-Havoc.htm, August 19, 2003.Google ScholarGoogle Scholar
  23. R. Pastor-Satorras and A. Vespignani. Epidemic spreading in scale-free networks. PRL, 86(14): 3200--3203, 2001.Google ScholarGoogle ScholarCross RefCross Ref
  24. R. Robinson. Dynamical Systems: Stability, Symbolic Dynamics, and Chaos (2dn Edition). CRC Press, 1999.Google ScholarGoogle Scholar
  25. C. Schneider-Mizell and L. Sander. A generalized voter model on complex networks. Journal of Statistical Physics, 136(1): 11, 2008.Google ScholarGoogle Scholar
  26. B. Schneier. Benevolent worms. http://www.schneier.com/blog/archives/2008/02/benevolent_worm_1.html, February 19, 2008.Google ScholarGoogle Scholar
  27. L. Shaughnessy. The internet: Frontline of the next war? http://www.cnn.com/2011/11/07/us/darpa/, November 7, 2011.Google ScholarGoogle Scholar
  28. P. Van Mieghem, J. Omic, and R. Kooij. Virus spread in networks. IEEE/ACM Trans. Netw., 17(1): 1--14, Feb. 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Y. Wang, D. Chakrabarti, C. Wang, and C. Faloutsos. Epidemic spreading in real networks: An eigenvalue viewpoint. In Proceedings of SRDS'03, pages 25--34.Google ScholarGoogle Scholar
  30. N. Weaver and D. Ellis. White worms don't work. ;login: The USENIX Magazine, 31(6): 33--38, 2006.Google ScholarGoogle Scholar
  31. H. S. N. Wire. Active cyber-defense strategy best deterrent against cyber-attacks. http://www.homelandsecuritynewswire.com/active-cyber-defense-strategy-best-deterrent-against-cyber-attacks, 28 June 2011.Google ScholarGoogle Scholar
  32. J. Wolf. Update 2-u.s. says will boost its cyber arsenal. http://www.reuters.com/article/2011/11/07/cyber-usa-offensive-idUSN1E7A61YQ20111107, November 7, 2011.Google ScholarGoogle Scholar
  33. M. Xu and S. Xu. An extended stochastic model for quantitative security analysis of networked systems. Internet Mathematics, 8(3): 288--320, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  34. S. Xu. Cybersecurity dynamics. In Proceedings of HotSoS'14, pages 14:1--14:2. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. S. Xu. Emergent behavior in cybersecurity. In Proceedings of HotSoS'14, pages 13:1--13:2. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. S. Xu, W. Lu, and H. Li. A stochastic model of active cyber defense dynamics. Internet Mathematics, 11(1): 23--61, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  37. S. Xu, W. Lu, and L. Xu. Push- and pull-based epidemic spreading in arbitrary networks: Thresholds and deeper insights. ACM TAAS, 7(3): 32:1--32:26, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. S. Xu, W. Lu, L. Xu, and Z. Zhan. Adaptive epidemic dynamics in networks: Thresholds and control. ACM TAAS, 8(4): 19, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. S. Xu, W. Lu, and Z. Zhan. A stochastic model of multivirus dynamics. IEEE TDSC, 9(1): 30--45, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Active cyber defense dynamics exhibiting rich phenomena

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      HotSoS '15: Proceedings of the 2015 Symposium and Bootcamp on the Science of Security
      April 2015
      170 pages
      ISBN:9781450333764
      DOI:10.1145/2746194
      • General Chair:
      • David Nicol

      Copyright © 2015 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 21 April 2015

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      HotSoS '15 Paper Acceptance Rate13of22submissions,59%Overall Acceptance Rate34of60submissions,57%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader