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Cloud Enterprise Dynamic Risk Assessment (CEDRA): a dynamic risk assessment using dynamic Bayesian networks for cloud

Dawood Behbehani1, Nikos Komninos1, Khalid Al-Begain2

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Summary
This summary is machine-generated.

This study introduces the Cloud Enterprise Dynamic Risk Assessment (CEDRA) model to quantify financial risks from cloud security vulnerabilities. The CEDRA model improves prediction accuracy for exploitations and associated monetary losses.

Keywords:
Cloud risk assessmentDynamic Bayesian NetworkQuantitative risk-analysis

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Risk Management

Background:

  • Cloud computing adoption accelerated during COVID-19, increasing digital strategy implementation.
  • Traditional risk assessment models inadequately quantify and monetize risks for business decisions.
  • There is a need for a model that assigns monetary values to risk consequences.

Purpose of the Study:

  • To propose a novel model for dynamic risk assessment in cloud environments.
  • To enable better understanding and quantification of financial risks associated with cloud security consequences.
  • To improve the prediction of vulnerability exploitations and their financial impact.

Main Methods:

  • Developed the Cloud Enterprise Dynamic Risk Assessment (CEDRA) model.
  • Integrated Common Vulnerability Scoring System (CVSS), threat intelligence, and exploitation data.
  • Utilized dynamic Bayesian networks for predicting vulnerability exploitations and financial losses.
  • Conducted a case study based on the Capital One breach.

Main Results:

  • The CEDRA model successfully assigns monetary loss values to risk consequences.
  • Experimental results demonstrated the model's applicability and effectiveness.
  • The proposed methods showed improvements in predicting both vulnerability exploitations and financial losses.

Conclusions:

  • The CEDRA model offers a significant advancement in quantifying financial risks in cloud computing.
  • This approach facilitates more informed, business-appropriate decision-making regarding cybersecurity investments.
  • The model provides enhanced accuracy in predicting the financial impact of cloud security incidents.