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JACIII Vol.19 No.5 pp. 676-680
doi: 10.20965/jaciii.2015.p0676
(2015)

Paper:

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Security Risk Assessment: Towards a Justification for the Security Risk Factor Table Model

Beverly Rivera*, Francisco Zapata**, and Vladik Kreinovich*

*Computational Science Program, University of Texas at El Paso
500 W. University, El Paso, TX 79968, USA

**Department of Industrial, Manufacturing, and Systems Engineering, University of Texas at El Paso
500 W. University, El Paso, TX 79968, USA

Received:
August 28, 2014
Accepted:
July 13, 2015
Published:
September 20, 2015
Creative Commons License
Keywords:
security risk, SRFT model, statistical justification
Abstract
One of the widely used methods to gauge risk is the Security Risk Factor Table (SRFT) model. While this model has been empirically successful, its use is limited by the fact that its formulas do not have a theoretical explanation – and thus, there is no guarantee that these formulas will work in other situations as well. In this paper, we provide a theoretical explanation for the SFRT formulas.
Cite this article as:
B. Rivera, F. Zapata, and V. Kreinovich, “Security Risk Assessment: Towards a Justification for the Security Risk Factor Table Model,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.5, pp. 676-680, 2015.
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References
  1. [1] I. Akgun, A. Kandakoglu, and A. F. Ozok, “Fuzzy integrated vulnerability assessment model for critical facilities in combating the terrorism”, Expert Systems with Applications, Vol.37, No.5, pp. 3561-3573, 2010.
  2. [2] American Petroleum Institute (API), “Security Guidelines for the Petroleum Industry,” Washington DC, 2003.
  3. [3] F. Aras, E. Karakas, and Y. Bic cen, “Fuzzy logic-based user interface design for risk assessment considering human factor: A case study for high-voltage cell,” Safety Science, Vol.70, pp. 387-396, 2014.
  4. [4] R. Arikan, M. Dagdeviren, and M. Kurt, “A Fuzzy Multi-Attribute Decision Making Model for Strategic Risk Assessment,” Int. J. of Computational Intelligence Systems, Vol.6, No.3, pp. 487-502, 2013.
  5. [5] L. Atymtayeva, A. Akzhalova, and K. Kozhakhmet, “Main Issues of the Software Development for Knowledge Base Processing in the Intelligent Applications for Information Security Audit,” J. Musi’c (Ed.), Recent Advances in Computer Engineering, Communications and Information Technology, Proc. of the 8th World Scientific Engineering Academy and Society (WSEAS) Int. Conf. on Computer Engineering and Applications (CEA’14), Tenerife, Spain, pp. 271-280, January 10-12, 2014.
  6. [6] S. Baijal and J. P. Gupta, “Site security for chemical process industries,” J. of Loss Prevention in the Process Industries, Vol.18, pp. 301-309, 2005.
  7. [7] S. Baijal and J. P. Gupta, “Securing oil and gas infrastructure,” J. of Petroleum Science and Engineering, Vol.55, pp. 174-186, 2007.
  8. [8] S. Baijal and J. P. Gupta, “Terror-Proofing Chemical Process Industries,” Process Safety and Environmental Protection, Vol.85, No.6, pp. 559-565, 2007.
  9. [9] S. Bajpai, A. Sachdeva, and J. P. Gupta, “Security risk assessment: Applying the concepts of fuzzy logic,” J. of Hazardous Materials, Vol.173, No.1-3, pp. 258-264, 2010.
  10. [10] H.-J. Kwak and G.-T. Park, “Image contrast enhancement for intelligent surveillance systems using multi-local histogram transformation,” J. of Intelligent Manufacturing, Vol.25, pp. 303-318, 2014.
  11. [11] J. Liu, L. Martinez, H. Wang, R. M. Rodriguez, and V. Novozhilov, “Computing with Words in Risk Assessment,” Int. J. of Computational Intelligence Systems, Vol.3, No.4, pp. 396-419, 2010.
  12. [12] M. Nematigoodarzi and M. Tavakoli, “Fuzzy Assessment of Vital Assets Risk in Oil Industry,” Kuwait Chapter of Arabian J. of Business and Management Review, Vol.3, No.12, pp. 295-302, 2014.
  13. [13] M. Nematigoodarzi, A. F. Farahani, S. E. Hosseini, and M. Tavakoli, “Phase Assessment of Vital Assets Risk in Oil Industry,” Int. J. of Biology, Pharmacy, and Applied Science, Vol.4, No.4, pp. 1858-1867, 2015.
  14. [14] A. Srivastava and J. P. Gupta, “New methodologies for security risk assessment of oil and gas industry,” Process Safety and Environmental Protection, Vol.88, No.6, pp. 407-412, 2010.
  15. [15] M. van Staalduinen and F. Khan, “A Barrier Based Methodology to Assess Site Security Risk,” Proc. of the Society of Petroleum Engineers (SPE) Exploration and Production (E&P) Health, Safety, Security and Environmental Conference–Americas, Denver, Colorado, USA, March 16-18, 2015.
  16. [16] N. Uttam and P. Verma, “Development of fuzzy model to integrate the human intuition with lead time estimation,” Proc. of the Conf. on Manufacturing Excellence: Imperative for Emerging Economies, organized by National Institute of Industrial Engineering (NITIE) and Production and Operations Management Society (POMS), Mumbai, India, December 18-21, 2014.
  17. [17] J. Aczéel and J. Dhombres, “Functional Equations in Several Variables,” Cambridge University Press, Cambridge, UK, 2008.

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