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thesis risk management framework

An Integrated Cybersecurity Risk Management (I-CSRM) Framework for Critical Infrastructure Protection

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TypePhD Thesis
Abstract

Risk management plays a vital role in tackling cyber threats within the Cyber-Physical System (CPS) for overall system resilience. It enables identifying critical assets, vulnerabilities, and threats and determining suitable proactive control measures to tackle the risks. However, due to the increased complexity of the CPS, cyber-attacks nowadays are more sophisticated and less predictable, which makes risk management task more challenging. This research aims for an effective Cyber Security Risk Management (CSRM) practice using assets criticality, predication of risk types and evaluating the effectiveness of existing controls. We follow a number of techniques for the proposed unified approach including fuzzy set theory for the asset criticality, machine learning classifiers for the risk predication and Comprehensive Assessment Model (CAM) for evaluating the effectiveness of the existing controls.
The proposed approach considers relevant CSRM concepts such as threat actor attack pattern, Tactic, Technique and Procedure (TTP), controls and assets and maps these concepts with the VERIS community dataset (VCDB) features for the purpose of risk predication. Also, the tool serves as an additional component of the proposed framework that enables asset criticality, risk and control effectiveness calculation for a continuous risk assessment. Lastly, the thesis employs a case study to validate the proposed i-CSRM framework and i-CSRMT in terms of applicability. Stakeholder feedback is collected and evaluated using critical criteria such as ease of use, relevance, and usability. The analysis results illustrate the validity and acceptability of both the framework and tool for an effective risk management practice within a real-world environment.
The experimental results reveal that using the fuzzy set theory in assessing assets' criticality, supports stakeholder for an effective risk management practice. Furthermore, the results have demonstrated the machine learning classifiers’ have shown exemplary performance in predicting different risk types including denial of service, cyber espionage, and Crimeware. An accurate prediction can help organisations model uncertainty with machine learning classifiers, detect frequent cyber-attacks, affected assets, risk types, and employ the necessary corrective actions for its mitigations.
Lastly, to evaluate the effectiveness of the existing controls, the CAM approach is used, and the result shows that some controls such as network intrusion, authentication, and anti-virus show high efficacy in controlling or reducing risks. Evaluating control effectiveness helps organisations to know how effective the controls are in reducing or preventing any form of risk before an attack occurs. Also, organisations can implement new controls earlier. The main advantage of using the CAM approach is that the parameters used are objective, consistent and applicable to CPS.

Year2021
PublisherUniversity of East London
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File 2021_PhD_Kure.pdf
Publication dates
05 Oct 2021
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01 Mar 2021
05 Oct 2021

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Vitor Sousa at Technical University of Lisbon

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Nuno Marques Almeida at University of Lisbon

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Ex-ante evaluation of a cross-sectorial business model for risk management in new product development: the case of Haitian farming

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  • Published: 09 September 2024

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thesis risk management framework

  • Rival Valcin   ORCID: orcid.org/0000-0003-1423-4097 1 ,
  • Tomohiro Uchiyama 2 ,
  • Rika Terano 2 ,
  • Katsumori Hatanaka 2 ,
  • Yasuo Ohe 2 &
  • Nina Shimoguchi 2  

With the rise of climate change, institutions are compelled to adopt new strategies to increase resilience toward natural disasters. For institutions providing insurance products to farmers, the probability of ruin becomes higher. How will agricultural institutions in regions with high occurrences of catastrophic risks survive? Since risks cannot be effectively managed exclusively in one sector, we have developed and evaluated a multisectoral business model in Haitian farming. To this end, in the summer of 2023, we interviewed 22 leaders of Haitian financial institutions. The research followed a framework outlining the different stages of new product development. This business analysis phase, which corresponds to stage four, is focused on evaluating the ex-ante business model. We used an interactive design approach for concept selection based on expert opinion. We prioritized intuitive assessment by experts to discover the most suitable implementation of the developed strategy. The study suggests that Minimum Extendable Compensation is the most suitable approach to risk management for the sectors involved, particularly regarding the principle of risk sharing and risk transfer. This new product is proposed to foster farmer resilience to natural disasters. In addition, partnership agreements promoting partial demonetization of transactions with the farmers are preferable to prevent liquidity limitation and possible moral hazards. Consequently, the derived approach can be applied to other small and medium economies, where the conventional agrarian insurance system presents itself as a massive burden for governments.

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The authors confirm that the data supporting this study's findings are available through the link below and may be visualized upon the authors' authorization.  https://docs.google.com/spreadsheets/d/1ve9UM6hezhU0bdIEW-e4UmUUGXmRkfZ0hy4fZt3Z_M4/edit?gid=236446349#gid=236446349 .

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Acknowledgements

We want to express our sincere gratitude for the anonymous reviewers' comments, which helped us improve the quality of the work. The patience, thoroughness, and supportive attitude expressed through the revision are more than inspiring. Our profound acknowledgments go to the Haitian Finance Institutions for their enthusiastic cooperation in the focus groups.

This research was supported by the “Tokyo Nodai Research Institute (TNRI) (grant number: 46407382H)” and the Bank of the Republic of Haiti (BRH).

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Rival Valcin

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Tomohiro Uchiyama, Rika Terano, Katsumori Hatanaka, Yasuo Ohe & Nina Shimoguchi

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As part of a Ph.D. dissertation, the study was conceived and designed by Rival Valcin. Rival Valcin also performed material preparation, data collection, and analysis. Rival Valcin wrote the first draft of the manuscript, and all co-authors commented on previous versions. All authors read and approved the final manuscript.

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Valcin, R., Uchiyama, T., Terano, R. et al. Ex-ante evaluation of a cross-sectorial business model for risk management in new product development: the case of Haitian farming. Rev Agric Food Environ Stud (2024). https://doi.org/10.1007/s41130-024-00220-1

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Received : 27 November 2023

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DOI : https://doi.org/10.1007/s41130-024-00220-1

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