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Evaluating and Applying Risk Remission Strategy Approaches to Prevent Prospective Failures in Information Systems

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17th International Conference on Information Technology–New Generations (ITNG 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1134))

Abstract

The article is devoted to a study related to applying various approaches to increase the dependability of Information Systems. In this article we evaluated and applied approaches such as RED (Risk in Early Design), GREEN (Generated Risk Event Effect Neutralization), and studied the possibility of using these approaches to prevent prospective failures in Information Systems. The MCDM (Multiple-Criteria Decision-Making) method was applied to assess information security risks in critical infrastructures. Based on this study we propose to use these methods and approaches as risk remission strategy tools in increasing the level of dependability of information systems. The proposed approach can be used to help in finding and neutralizing relevant risks.

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Acknowledgments

This work was done as part of a research grant №AP05131784 of the Ministry of Education and Science of the Republic of Kazakhstan for 2018–2020.

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Authors and Affiliations

  1. Computer Science Department, Nazarbayev University, Nur-Sultan, Kazakhstan

    Askar Boranbayev

  2. Information Systems Department, L.N. Gumilyov Eurasian National University, Nur-Sultan, Kazakhstan

    Seilkhan Boranbayev & Askar Nurbekov

Authors
  1. Askar Boranbayev
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  2. Seilkhan Boranbayev
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  3. Askar Nurbekov
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Corresponding author

Correspondence to Seilkhan Boranbayev .

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Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, Las Vegas, NV, USA

    Shahram Latifi

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Boranbayev, A., Boranbayev, S., Nurbekov, A. (2020). Evaluating and Applying Risk Remission Strategy Approaches to Prevent Prospective Failures in Information Systems. In: Latifi, S. (eds) 17th International Conference on Information Technology–New Generations (ITNG 2020). Advances in Intelligent Systems and Computing, vol 1134. Springer, Cham. https://doi.org/10.1007/978-3-030-43020-7_87

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  • DOI: https://doi.org/10.1007/978-3-030-43020-7_87

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-43019-1

  • Online ISBN: 978-3-030-43020-7

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