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Application of Bayesian Algorithm in Risk Quantification for Network Security.

Lei Wei1

  • 1School of Criminal Justice, Shanghai University of Political Science and Law, Shanghai 201701, China.

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|July 18, 2022
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Summary
This summary is machine-generated.

This study introduces a Bayesian algorithm model for quantifying network security risks, integrating expert knowledge with objective data. The model effectively quantifies risks, achieving high accuracy in network security assessments.

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Area of Science:

  • Computer Science
  • Information Security
  • Risk Management

Background:

  • Network security risk quantification is complex, involving technical and management factors with inherent uncertainties.
  • Current methods for fully objective network information security risk quantification are not yet mature.
  • Quantifying risks from diverse threat sources remains a significant challenge in cybersecurity.

Purpose of the Study:

  • To develop and validate a network security risk quantification model using a Bayesian algorithm.
  • To integrate expert knowledge with objective assessment data for improved risk analysis.
  • To enhance the continuity and accumulation of security assessments in dynamic network environments.

Main Methods:

  • A Bayesian algorithm-based network security risk quantification model was developed.
  • Expert knowledge was used to define the conditional probability matrix for the Bayesian inference.
  • Subjective expert judgments on damage degrees were synthesized into prior information for threat assessment.
  • Objective assessment information was incorporated through observation nodes in the Bayesian network.
  • The model combined subjective threat levels with objective data for continuous security assessment.

Main Results:

  • The proposed model effectively quantifies network security risks from various threat sources.
  • Integration of expert knowledge and objective data yielded a robust risk assessment framework.
  • The Bayesian algorithm facilitated the continuous accumulation of security assessment information.
  • The model demonstrated a high level of accuracy, with an error rate of approximately 3%.

Conclusions:

  • The Bayesian algorithm model offers a promising approach for network security risk quantification.
  • The method successfully bridges the gap between subjective expert insights and objective data in risk assessment.
  • The developed model shows significant potential for improving the accuracy and reliability of network security risk management.