Peer review quantitative

A Quantitative Methodology for Cloud Security Risk Assessment by Basu, Sengupta, and Mazumdar.

Academic Peer Review

  1. Appropriateness of Research Methodology The authors employ a quantitative risk assessment methodology tailored specifically for cloud computing environments. This approach is suitable given the paper’s objective of quantifying risks based on asset dependencies, vulnerabilities, and threats. The study introduces a mathematical risk modeling framework, addressing gaps in previous qualitative or overly generic risk assessment techniques.

However, while the methodology is mathematically rigorous, its real-world applicability remains untested beyond theoretical validation. The authors acknowledge this limitation, suggesting future validation within actual cloud environments. Integrating empirical validation (e.g., applying the model to real-world cloud infrastructures) would strengthen its credibility.

  1. Appropriateness of Data Collection and Analysis Unlike empirical studies that rely on observed data, this paper constructs a risk assessment framework using theoretical models. The methodology defines risk components—Asset Value (AV), Vulnerabilities (Severity, Exploitability), Threats (Likelihood of Occurrence), and Security Concern (SC)—and integrates them into a quantitative scoring system.

This analytical approach is sound in measuring cloud security risks numerically, but it depends on assumed probability distributions rather than real-world data. The case study offers a hypothetical application of the framework, but it does not include actual security incidents, penetration testing results, or historical risk data. Including empirical data from industry case studies would validate the model’s accuracy.

  1. Support for Claims and Conclusions The paper is well-supported by references to ISO/IEC security standards, NIST guidelines, and prior research on cloud security risks. The authors justify the need for a quantitative approach by citing limitations in existing qualitative methods (e.g., ENISA’s risk scenarios and CSA’s security assessments).

However, while the mathematical formulations are clear, the absence of real-world validation weakens their practical impact. The claim that the proposed framework is superior to existing methodologies is theoretically valid but lacks comparative performance benchmarks. Future research should apply this methodology to diverse cloud service providers (CSPs) and compare outcomes against existing frameworks.

  1. Enhancements to the Paper To improve the study, I recommend the following enhancements:

Empirical Validation: Apply the framework to real-world cloud service providers and compare results with actual security incidents or expert assessments. Comparative Analysis: Benchmark the proposed risk assessment method against existing frameworks (e.g., ENISA, CSA’s CCM) to demonstrate its advantages. Automation & Tool Development: Developing a software tool to automate risk calculations would enhance usability for practitioners. Broader Case Studies: The current case study focuses on a single cloud provider (CSP1); expanding to multiple CSPs and cloud architectures would improve generalizability. Validation via Security Testing: Testing the model against penetration testing reports, security audits, or simulated cyberattacks would demonstrate its real-world reliability.

References:

Basu, S., Sengupta, A. and Mazumdar, C. (2017) “A quantitative methodology for cloud security risk assessment,” in CLOSER 2017 - Proceedings of the 7th International Conference on Cloud Computing and Services Science. Available at: https://doi.org/10.5220/0006294401200131.