A New Formal Description Model of Network Attacking and Defence Knowledge of Oil and Gas Field SCADA System

  • Li Yang
  • Xiedong Cao
  • Jie Li
  • Cundang Wei
  • Shiyong Cao
  • Dan Zhang
  • Zhidi Chen
  • Gang Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7234)

Abstract

In this paper, we analyse the factors affecting the network security of a gas SCADA system. We model the security problem in the SCADA system into an online digital intelligent defensing process, including all reasoning judgment, thinking and expression in attacking and defence. This model abstracts and establishes a corresponding and equivalent network attacking and defence knowledge system. Also, we study the formal knowledge theory of SCADA network for oil and gas fields though exploring the factors state space, factors express, equivalence partitioning etc, and then put forward a network attack effect fuzzy evaluation model using factor neural network theory. The experimental results verify the effectiveness of the model and the algorithm, which lays the foundation for the research of the simulation method.

Keywords

SCADA Factors Knowledge Knowledge Description Factors Express 

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References

  1. 1.
    Ragsdale, D.J., Surdu, J.R., Carver, C.A.: Information assurance education through active learning.The IWAR Laboratory (2002)Google Scholar
  2. 2.
    De Vivo, M., de Vivo, G.O., Isern, G.: Internet security attacks at the basic levels. Operating Systems Review 32(2) (2002)Google Scholar
  3. 3.
    Teo, L., Zheng, Y., Ahn, G.: Intrusion detection force: an infrastructure for internet-scale intrusion detection. In: Proceedings of the First IEEE International Workshop on Information Assurance (IWIA 2003), Darmstadt, Germany, pp. 73–91 (2003)Google Scholar
  4. 4.
    Zheng, L., Liu, Z., Wu, Y.: Network warfare on the battlefield. Military science press, Beijing (2002)Google Scholar
  5. 5.
    Liu, Z., Liu, Y.: Factor neural network theory and implementation strategy research. Beijing Normal University Press, Beijing (1992)Google Scholar
  6. 6.
    Zhang, S., Tang, C., Zhang, Q., et al.: Based on the efficiency of network attack against method classification and formalism description. Information and Electronic Engineering 2(3), 161–167 (2004)Google Scholar
  7. 7.
    Huang, G., Ren, D.: Based on of both-branch fuzzy decision and fuzzy Petri nets the attackStrike model. Computer Applications 27(11), 2689–2693 (2007)MathSciNetGoogle Scholar
  8. 8.
    Huang, G., Qiao, K., Zhu, H.: Based on the fuzzy attack FPN graph model and productionAlgorithm. J. Microelectronics and Computer (5), 162–165 (2007)Google Scholar
  9. 9.
    Guo, C., Liu, Z., et al.: The virtual network attack and defense analysis model. Computer Engineering and Applications 44(25), 100–103 (2008)Google Scholar
  10. 10.
    Wang, P.Z., Sugeno, M.: The factors field and back-ground structure for fuzzy subsets. Fuzzy Mathematics 2(2), 45–54 (1982)MathSciNetMATHGoogle Scholar
  11. 11.
    Guo, C.-X., Liu, Z.-L., Miao, Q.: Network attack planning model and its generating algorithm. Computer Engineering and Applications 46(31), 121–123 (2010)Google Scholar
  12. 12.
    Guo, C.-X., Liu, Z.-L., Zhang, Z.-N., Tao, Y.: Network Attack Knowledge Model Based on Factor Space Theory. Telecommunication Engineering 49(10), 11–14 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Li Yang
    • 1
  • Xiedong Cao
    • 1
  • Jie Li
    • 1
  • Cundang Wei
    • 1
  • Shiyong Cao
    • 1
  • Dan Zhang
    • 1
  • Zhidi Chen
    • 1
  • Gang Tang
    • 2
  1. 1.Southwest Petroleum UniversityChengduP.R. China
  2. 2.Sinopec Southwest Petroleum BranchP.R. China

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