Communication Security Prognosis Realized as the Parallel Dynamic Auditing Intelligent System

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 296)

Abstract

Communication operations are realized according to cryptography protocols in a typical network. Such communication among users takes place on the basis of public keys, secrets, supplies, encrypted messages and nonces. The investigation of the communication run gives security information about forthcoming threats. The main goal of the research consists in the elaboration of a useful and simple (in the sense of complexity) prognosis algorithm adapted to an auditing form of investigation. The results of the prognosis presented in the time parameter about impending threats are dynamically changed, operation by operation. Therefore, users can prepare a strategy of avoiding the closest (in the sense of time) or the most dangerous threat. The proposed approach is based on probability counting rules that guarantee fast realization at the cost of accuracy. The large scale of parallelization possibilities is also worth noticing. This follows from the independent module structure of fundamental security elements which are associated with dynamically designated counting threads.

Keywords

protocol logic probabilistic timed automata communication security prognosis 

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References

  1. 1.
    Tadeusiewicz, R.: Introduction to Inteligent Systems. In: Wilamowski, B.M., Irvin, J.D. (eds.) The Industrial Electronic Handbook, ch. 1, pp. 1-1 – 1-12. CRC Press, Boca Raton (2011)Google Scholar
  2. 2.
    Tadeusiewicz, R.: Place and role of Intelligence Systems in Computer Science. Computer Methodsin Material Science 10(4), 193–206, 13. Tadeusiewicz, R.: Place and role of Intelligence Systems in Computer Science. Computer Methodsin Material Science 10(4), 193–206 (2010)Google Scholar
  3. 3.
    Kwiatkowska, M., Norman, R., Sproston, J.: Symbolic Model Checking of Probabilistic Timed Automata Using Backwards Reachability. Tech. rep. CSR-03-10, University of Birmingham (2003)Google Scholar
  4. 4.
    Kwiatkowska, M., Norman, G., Segala, R., Sproston, J.: Automatic Verification of Real-time Systems with Discrete Probability Distribution. Theoretical Computer Science 282, 101–150 (2002)CrossRefMATHMathSciNetGoogle Scholar
  5. 5.
    Evans, N., Schneider, S.: Analysing Time Dependent Security Properties in CSP Using PVS. In: Cuppens, F., Deswarte, Y., Gollmann, D., Waidner, M. (eds.) ESORICS 2000. LNCS, vol. 1895, pp. 222–237. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  6. 6.
    Focardi, R., Gorrieri, R., Martinelli, F.: Information Flow Analysis in a Discrete -Time Process Algebra. In: Proc. of 13th CSFW, pp. 170–184. IEEE CS Press (2000)Google Scholar
  7. 7.
    Gray III, J.W.: Toward a Mathematical Foundation for Information Flow Security. Journal of Computer Security 1, 255–294 (1992)Google Scholar
  8. 8.
    Alur, R., Courcoubetis, C., Dill, D.L.: Verifying Automata Specifications of Probabilistic Real- Time Systems. In: Huizing, C., de Bakker, J.W., Rozenberg, G., de Roever, W.-P. (eds.) REX 1991. LNCS, vol. 600, pp. 28–44. Springer, Heidelberg (1992)CrossRefGoogle Scholar
  9. 9.
    Alur, R., Dill, D.L.: A Theory of Timed Automata. Theoretical Computer Science 126, 183–235 (1994)CrossRefMATHMathSciNetGoogle Scholar
  10. 10.
    Szpyrka, M.: Fast and exible modeling of real-time systems with RTCP- nets. Computer Science, 81–94 (2004)Google Scholar
  11. 11.
    Burrows, M., Abadi, M., Needham, R.: A Logic of Authentication. In: Harper, R. (ed.) Logics and Languages for Security, pp. 815–819 (2007), Di Pierro, A., Hankin, C., Wiklicky, H.: Approximate Non-Interference. Journal of Computer Security 12, 37–82 (2004)Google Scholar
  12. 12.
    Piech, H., Grodzki, G.: The system conception of investigation of the communication security level in networks. In: Abramowicz, W. (ed.) BIS Workshops 2013. LNBIP, vol. 160, pp. 148–159. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  13. 13.
    Beauquier, D.: On Probabilistic Timed Automata. Theoretical Computer Science 292, 65–84 (2003)CrossRefMATHMathSciNetGoogle Scholar
  14. 14.
    Focardi, R., Gorrieri, R.: A Classification of Security Properties. Journal of Computer Security 3, 5–33 (1995)Google Scholar
  15. 15.
    Tudruj, M., Masko, L.: Toward Massively Parallel Computation based on Dynamic Clasters with Communication on the Fly. In: IS on Parallel and Distributed Computing, Lille, France, pp. 155–162 (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Czestochowa University of TechnologyCzestochowaPoland

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