Vulnerability-Aware Architecture for a Tactical, Mobile Cloud

  • Anne-Laure JousselmeEmail author
  • Kevin Huggins
  • Nicolas Léchevin
  • Patrick Maupin
  • Dominic Larkin
Part of the Studies in Computational Intelligence book series (SCI, volume 424)


Currently light infantry soldiers do not have access to many of their cyber resources the moment they depart the forward operating base (FOB). Commanders with recent combat experience have reported on the dearth of computing abilities once a mission is underway [14]. To address this, our group seeks to develop a tactical, mobile cloud implemented on a swarm of semi-autonomous robots. We provide two contributions with this work. First, provide a formal definition of the problem followed by a description of our approach to vulnerable state identification based on pattern recognition techniques. Second, we present an awareness definition as it pertains to our domain.


Vulnerability Assessment Communication Range Mobile Cloud Network Vulnerability Robot Network 
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.
    Al Mannay, W.I., Lewis, T.G.: Minimizing network risk with application to critical structure protection. Journal of Information Warfare 6(2), 52–68 (2007)Google Scholar
  2. 2.
    Brown, G.G., Carlyle, W.M., Salmeron, J., Wood, K.: Analyzing the vulnerability of critical infrastructure to attack and planning defenses. Tutorial in Operations Research, Informs, 102–123 (2005)Google Scholar
  3. 3.
    Csárdi, G., Nepusz, T.: The igraph software package for complex network research. InterJournal Complex Systems 1695 (2006)Google Scholar
  4. 4.
    Dobson, I.: Distance to Bifurcation in Multidimensional Parameter Space: Margin Sensitivity and Closest Bifurcations. In: Chen, D.J., Hill, X., Yu, X. (eds.) Bifurcation Control. LNCS, vol. 293, pp. 49–66. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    Garcia, M.L.: Vulnerability assessment of physical protection systems. Butterworth-Heinemann (2006)Google Scholar
  6. 6.
    Godsile, C., Royle, G.: Algebraic Graph Theory. Springer, New York (2001)CrossRefGoogle Scholar
  7. 7.
    Haimes, Y.Y.: On the definition of vulnerabilities in measuring risk to infrastructure. Risk Analysis 26(2), 293–296 (2006)CrossRefGoogle Scholar
  8. 8.
    Halpern, J., Moses, Y., Vardi, M.Y.: Algorithmic knowledge. In: Proc. of the 5th Conference on Theoretical Aspects of Reasoning about Knowledge (TARK 1994), pp. 255–266. Morgan Kaufmann (1994)Google Scholar
  9. 9.
    Jousselme, A.-L., Maupin, P., Garion, G., Cholvy, L., Saurel, C.: Situation awareness and ability in coalitions. In: 10th International Conference on Information Fusion, Quebec city, Canada, July 9-12 (2007)Google Scholar
  10. 10.
    Klibi, W., Martel, A., Guitouni, A.: The design of robust value-creating supply-chain network: a review. European Journal of Operational Research 203(2), 283–293 (2010)zbMATHCrossRefGoogle Scholar
  11. 11.
    Larsen, H.L., Yager, R.R.: A Framework for Fuzzy Recognition Technology. IEEE Trans. on Systems, Man, and Cybernetics - Part C: Applications and Reviews 30(1), 65–76 (2000)CrossRefGoogle Scholar
  12. 12.
    Léchevin, N., Rabbath, C.A., Maupin, P.: Toward a stability monitoring system of an asset-communications network exposed to malicious attacks. In: American Control Conf., San Francisco (2011)Google Scholar
  13. 13.
    Léchevin, N., Jousselme, A.-L., Maupin, P.: Pattern Recognition Framework for the Prediction of Network Vulnerabilities. In: IEEE Network Science Workshop, West Point, NY (June 2011)Google Scholar
  14. 14.
    Levine, C.: Analysers, and Users of Situational Information. In: Workshop on Information Sharing at the Front Line, Indian Wells, CA (April 2010)Google Scholar
  15. 15.
    Maupin, P., Jousselme, A.-L., Wehn, H., Mitrovic-Minic, S., Happe, J.: A Situation Analysis Toolbox: Application to Coastal and Offshore Surveillance. In: Int. Conf. on Information Fusion, Edinburgh, UK (2010)Google Scholar
  16. 16.
    McGill, W.L., Ayyub, B.M.: The meaning of vulnerability in the context of critical infrastructure protection. In: Critical Infrastructure Protection: Element of Risk, Critical Infrastructure Protection Program, George Mason University School of Law (2007)Google Scholar
  17. 17.
    Nagurnay, A., Quiang, Q.: A network efficiency measure with application to critical infrastructure networks. Journal of Global Optimization 40, 261–275 (2008)CrossRefGoogle Scholar
  18. 18.
    Pradhan, Hansen, A., Hemmer, P.C.: Crossover behavior in burst avalanches: signature of imminent failure. Physical Review Letters 95(12), 125501-1(4) (2005)CrossRefGoogle Scholar
  19. 19.
    Ramos, O., Altshuler, E., Maloy, K.J.: Avalanche prediction in a self-organized pile of beads. Physical Review Letter 102(7), 078701(1-4) (2009)CrossRefGoogle Scholar
  20. 20.
    Shmueli, G., Fienberg, S.E.: Current and potential statistical methods for monitoring multiple data streams for bio-surveillance. In: Statistical Methods in Counter-Terrorism, pp. 109–140. Springer (2006)Google Scholar
  21. 21.
    Sprague, K.B., Dobias, P.: Behavior in Simulated Combat – Adaptation and Response to Complex Systems Factors, DRDC CORA TM 2008-044 (2008)Google Scholar
  22. 22.
    Stamatelatos, M., Vesely, W., Dugan, J., Fragola, J., Minarick, J., Railsback, J.: Fault tree handbook with aerospace applications. In: NASA Office of Safety and Mission Assurance, Washington, DC (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Anne-Laure Jousselme
    • 1
    Email author
  • Kevin Huggins
    • 2
  • Nicolas Léchevin
    • 1
  • Patrick Maupin
    • 1
  • Dominic Larkin
    • 2
  1. 1.Defence R&D Canada–ValcartierQuebecCanada
  2. 2.US Military AcademyWest PointUSA

Personalised recommendations