Hybrid Dependencies Between Cyber and Physical Systems

  • Sandra KönigEmail author
  • Stefan Rass
  • Benjamin Rainer
  • Stefan Schauer
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 998)


Situational awareness is often a matter of detailed local information and proportionally limited view on the bigger picture. Conversely, the big picture avoids complicating details, and as such displays the system components as atomic “black boxes”. This work proposes a combination of local and global views, accounting for a common practical division of physical and cyber domains, each of which have their own group of experts and management processes. We identify a small set of data items that is required about the physical and cyber parts of a system, along with a high-level description of how these parts interoperate. From these three ingredients, which we call physical, cyber and hybrid “awareness” (meaning just knowledge about what is there), we construct a simulation model to study cascading effects and indirect implications of distortions in a cyber-physical system. Our simulation model is composed from coupled Mealy automata, and we show an instance of it using a small cyber-physical infrastructure. This extends the awareness from “knowing what is” to “knowing what could happen next”, and as such addresses a core duty of effective risk management. Manifold extensions to this model are imaginable and discussed in the aftermath of the definition and example demonstration.


Security Cascading effects Probabilistic automata Critical infrastructure 



This work was supported by the European Commission’s Project SAURON (Scalable multidimensional situation awareness solution for protecting European ports) under the HORIZON 2020 Framework (Grant No. 740477).


  1. 1.
    Bañuls, V.A., Turoff, M.: Scenario construction via delphi and cross-impact analysis. Technol. Forecast. Soc. Change 78(9), 1579–1602 (2011). The Delphi technique: Past, present, and future prospectsCrossRefGoogle Scholar
  2. 2.
    Carreras, B.A., Newman, D.E., Gradney, P., Lynch, V.E., Dobson, I.: Interdependent risk in interacting infrastructure systems. In: 40th Annual Hawaii International Conference on System Sciences, HICSS 2007, p. 112, January 2007Google Scholar
  3. 3.
    Dobson, I.: Estimating the propagation and extent of cascading line outages from utility data with a branching process. IEEE Trans. Power Syst. 27(4), 2146–2155 (2012)CrossRefGoogle Scholar
  4. 4.
    Gordon, T., Hayward, H.: Initial experiments with the cross impact matrix method of forecasting. Futures 1(2), 100–116 (1968). Scholar
  5. 5.
    Guo, H., Zheng, C., Iu, H.H.C., Fernando, T.: A critical review of cascading failure analysis and modeling of power system. Renew. Sustain. Energy Rev. 80, 9–22 (2017)CrossRefGoogle Scholar
  6. 6.
    König, S., Gouglidis, A.: Random damage in interconnected networks, pp. 185–201. Springer, Cham (2018)CrossRefGoogle Scholar
  7. 7.
    König, S., Schauer, S., Rass, S.: A stochastic framework for prediction of malware spreading in heterogeneous networks. In: Proceedings of NordSec Conference 2016 Secure IT Systems, Oulu, Finland, pp. 67–81. Springer (2016)Google Scholar
  8. 8.
    Kotzanikolaou, P., Theoharidou, M., Gritzalis, D.: Cascading effects of common-cause failures in critical infrastructures. In: Butts, J., Shenoi, S. (eds.) Critical Infrastructure Protection VII, pp. 171–182. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  9. 9.
    Massaro, E., Bagnoli, F.: Epidemic spreading and risk perception in multiplex networks: a self-organized percolation method. Phys. Rev. E 90(5), 052817 (2014). Scholar
  10. 10.
    McGee, S., Frittman, J., James Ahn, S., Murray, S.: Implications of cascading effects for the hyogo framework. Int. J. Disaster Resil. Built Environ. 7, 144–157 (2016)CrossRefGoogle Scholar
  11. 11.
    Ouyang, M.: Review on modeling and simulation of interdependent critical infrastructure systems. Reliab. Eng. Syst. Saf. 121, 43–60 (2014). Scholar
  12. 12.
    Pagani, G.A., Aiello, M.: The power grid as a complex network: a survey. Phys. A: Stat. Mech. Appl. 392(11), 2688–2700 (2013)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Paz, A., Rheinboldt, W.: Introduction to Probabilistic Automata. Elsevier Science, Burlington (2014). Scholar
  14. 14.
    Qi, J., Dobson, I., Mei, S.: Towards estimating the statistics of simulated cascades of outages with branching processes. IEEE Trans. Power Syst. 28(3), 3410–3419 (2013)CrossRefGoogle Scholar
  15. 15.
    Rabin, M.O.: Probabilistic automata. Inform. Control 6(3), 230–245 (1963). Scholar
  16. 16.
    Rahnamay-Naeini, M., Hayat, M.M.: Cascading failures in interdependent infrastructures: an interdependent Markov-chain approach. IEEE Trans. Smart Grid, pp. 1997–2006. (2016). Scholar
  17. 17.
    Rahnamay-Naeini, M., Wang, Z., Ghani, N., Mammoli, A., Hayat, M.M.: Stochastic analysis of cascading-failure dynamics in power grids. IEEE Trans. Power Syst., pp. 1767–1779. (2014). Scholar
  18. 18.
    SAURON: Scalable multidimensionAl sitUation awaReness sOlution for protectiNg european ports, December 2018.
  19. 19.
    Troina, A.: Probabilistic Timed Automata for Security Analysis and Design. CreateSpace Independent Publishing Platform, Scotts Valley (2017)Google Scholar
  20. 20.
    Turoff, M.: An alternative approach to cross impact analysis. Technol. Forecast. Soc. Change 3, 309–339 (1971). Scholar
  21. 21.
    Wang, Z., Scaglione, A., Thomas, R.J.: A Markov-transition model for cascading failures in power grids. In: 2012 45th Hawaii International Conference on System Sciences. IEEE, January 2012.
  22. 22.
    Wu, S.J., Chu, M.T.: Markov chains with memory, tensor formulation, and the dynamics of power iteration. Appl. Math. Comput. 303(C), 226–239 (2017). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sandra König
    • 1
    Email author
  • Stefan Rass
    • 2
  • Benjamin Rainer
    • 1
  • Stefan Schauer
    • 1
  1. 1.Center for Digital Safety and SecurityAustrian Institute of Technology GmbHViennaAustria
  2. 2.Institute of Applied Informatics, System Security GroupUniversität KlagenfurtKlagenfurtAustria

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