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Security Models Based on Stochastic Meta Data

  • Alexander Grusho
  • Elena Timonina
  • Sergey Shorgin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10684)

Abstract

Earlier for support of information security of network interactions a control of network connections by means of meta data was suggested. Meta data contain information on admissible interactions of tasks and positions of applications for their solution in a distributed network.

This security mechanism can be attacked. To prevent found vulnerabilities an extension of meta data was built. It was necessary to consider stochastic elements in extended meta data. Control of network interconnections based on stochastic meta data was investigated.

Keywords

Distributed information system Software-Defined Network Network security Meta data Network vulnerabilities Probability distributions 

Notes

Acknowledgements

The research is supported by Russian Science Foundation (project 16-11-10227).

References

  1. 1.
    Samuylov, K.E., Chukarin, A.V., Yarkina, N.V.: Business Processes and Information Technologies in Management of the Telecommunication Companies. Alpina Pablisherz, Moscow (2009)Google Scholar
  2. 2.
    Grusho, A.A., Timonina, E.E., Shorgin, S.Ya.: Modelling for ensuring information security of the distributed information systems. In: Proceedings of 31th European Conference on Modelling and Simulation, ECMS 2017, pp. 656–660. Digitaldruck Pirrot GmbHP, Germany (2017)Google Scholar
  3. 3.
    Grusho, A., Grusho, N., Zabezhailo, M., Piskovski, V., Timonina, E.: Information security of SDN on the basis of meta simulation. In: Proceedings of 7th International Conference on Mathematical Methods, Models, and Architectures for Computer Network Security, MMM-ACNS 2017, Warsaw, Poland (2017, to be published)Google Scholar
  4. 4.
    Luo, S., Wu, J., Li, J., Guo, L., Pei, B.: Context-aware traffic forwarding service for applications in SDN. In: Proceedings of 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), pp. 557–561 (2015).  https://doi.org/10.1109/SmartCity.2015.128
  5. 5.
    Sherry, J., Ratnasamy, S.: A survey of enterprise middlebox deployments. Technical report UCB/EECS-2012-24, EECS Department, Universaty of California, Bercley, February 2012Google Scholar
  6. 6.
    Grusho, A.A., Grusho, N.A., Timonina, E.E.: Content analysis in information flows. J. AIP Conf. Proc. 1738, 220002–2200024 (2016).  https://doi.org/10.1063/1.4952001 CrossRefzbMATHGoogle Scholar
  7. 7.
    Charlyy, D.J., Nikitin, E.S., Antoshina, E.J.: A simple information flow security model for software-defined network. In: Proceedings of the 17th Conference of FRUCT Association, pp. 276–282 (2015)Google Scholar
  8. 8.
    Shu, Z., Wan, J., Li, D., Lin, J., Vasilakos, A.V., Imran, M.: Security in Software-defined networking: threats and countermeasures. J. Mobile Netw. Appl. 21(5), 764–776 (2016).  https://doi.org/10.1007/s11036-016-0676-x CrossRefGoogle Scholar
  9. 9.
    Scott-Hayward, S., O’Callaghan, G., Sezer, S.: SDN security: a survey. In: Proceedings of 2013 IEEE SDN for Future Networks and Services (SDN4FNS), pp. 1–7 (2013).  https://doi.org/10.1109/SDN4FNS.2013.6702553
  10. 10.
    Kreutz, D., Ramos, F.M.V., Verissimo, P.: Towards secure and dependable software-defined networks. In: Proceedings of the 2nd ACM SIGCOMM Workshop on Hot Topics in Software Defined Networking, HotSDN 2013, pp. 55–60. ACM, New York (2013).  https://doi.org/10.1145/2491185.2491199
  11. 11.
    Booch, G., Rumbaugh, J., Jacobson, I.: The Unified Modeling Language User Guide. Addison Wesley, New York (1999)Google Scholar
  12. 12.
    Tanayev, V.S., Shkurba, V.V.: Introduction to the scheduling theory. Science, Moscow (1975). (in Russian)Google Scholar
  13. 13.
    Peterson, J.L.: Petri Net Theory and the Modeling of Systems. Prentice-Hall Inc, N.J. (1981)zbMATHGoogle Scholar
  14. 14.
    Knuth, D.E.: The Art of Computer Programming. Fundamental Algorithms, vol. 2, 3rd edn. Addison-Wesley, Massachusetts (1997)zbMATHGoogle Scholar
  15. 15.
    Nilsson, N.J.: Problem-Solving Methods in Artificial Intelligence. McGraw-Hill Pub. Co., New York (1971)Google Scholar
  16. 16.
    Grusho, A.A., Zabezhailo, M.I., Zatsarinnyy, A.A., Nikolaev, A.V., Piskovski, V.O., Timonina, E.E.: Erroneous states classifications in dictributed computing systems and sources of their occurences. Syst. Means Inf. 27(2), 29–40 (2017).  https://doi.org/10.14357/08696527170203 Google Scholar
  17. 17.
    Grusho, A., Grusho, N., Timonina, E.: Problems of modeling in the analysis of covert channels. In: Kotenko, I., Skormin, V. (eds.) MMM-ACNS 2010. LNCS, vol. 6258, pp. 118–124. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-14706-7_9 CrossRefGoogle Scholar
  18. 18.
    Grusho, A., Timonina, E.: Prohibitions in discrete probabilistic statistical problems. Discrete Math. Appl. 21(3), 275–281 (2011).  https://doi.org/10.1515/dma.2011.016 MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Grusho, A., Timonina, E.: Consistent sequences of tests defined by bans. In: Migdalas, A., Sifaleras, A., Georgiadis, C., Papathanasiou, J., Stiakakis, E. (eds.) Springer Proceedings in Mathematics and Statistics and Optimization Theory, Decision Making, and Operations Research Applications, vol. 31. Springer, New York (2013).  https://doi.org/10.1007/978-1-4614-5134-1_20 Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Alexander Grusho
    • 1
  • Elena Timonina
    • 1
  • Sergey Shorgin
    • 1
  1. 1.Institute of Informatics ProblemsFederal Research Center “Computer Science and Control” of the Russian Academy of SciencesMoscowRussia

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