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Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

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Risk Assessment For Industrial Control Systems Quantifying Availability Using Mean Failure Cost (MFC)

Qian Chen
  • Engineering Technology, Savannah State University, Savannah, GA 31404 USA
/ Robert K. Abercrombie
  • Computational Science and Engineering, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA, Department of Computer Science, University of Memphis, Memphis, TN 38152 USA
/ Frederick T. Sheldon
  • Department of Computer Science, University of Memphis, Memphis, TN 38152 USA
Published Online: 2015-09-23 | DOI: https://doi.org/10.1515/jaiscr-2015-0029

Abstract

1 Industrial Control Systems (ICS) are commonly used in industries such as oil and natural gas, transportation, electric, water and wastewater, chemical, pharmaceutical, pulp and paper, food and beverage, as well as discrete manufacturing (e.g., automotive, aerospace, and durable goods.) SCADA systems are generally used to control dispersed assets using centralized data acquisition and supervisory control.

Originally, ICS implementations were susceptible primarily to local threats because most of their components were located in physically secure areas (i.e., ICS components were not connected to IT networks or systems). The trend toward integrating ICS systems with IT networks (e.g., efficiency and the Internet of Things) provides significantly less isolation for ICS from the outside world thus creating greater risk due to external threats. Albeit, the availability of ICS/SCADA systems is critical to assuring safety, security and profitability. Such systems form the backbone of our national cyber-physical infrastructure.

Herein, we extend the concept of mean failure cost (MFC) to address quantifying availability to harmonize well with ICS security risk assessment. This new measure is based on the classic formulation of Availability combined with Mean Failure Cost (MFC). The metric offers a computational basis to estimate the availability of a system in terms of the loss that each stakeholder stands to sustain as a result of security violations or breakdowns (e.g., deliberate malicious failures).

Footnotes

  • 1This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy (DOE). The United States Government (USG) retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for USG purposes. The DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

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About the article

Published Online: 2015-09-23

Published in Print: 2015-07-01


1This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy (DOE). The United States Government (USG) retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for USG purposes. The DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).


Citation Information: Journal of Artificial Intelligence and Soft Computing Research, ISSN (Online) 2083-2567, DOI: https://doi.org/10.1515/jaiscr-2015-0029. Export Citation

© Academy of Management (SWSPiZ), Lodz. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)

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