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Continuous Quantitative Risk Management in Smart Grids Using Attack Defense Trees.

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Summary

This study introduces a new method for continuously assessing cyber risks in Internet of Things (IoT) smart grids. It enables optimized security strategies by quantitatively analyzing attack and defense scenarios for better decision-making.

Keywords:
information securityrisk assessmentsecurity management

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Area of Science:

  • Cybersecurity
  • Smart Grid Systems
  • Risk Assessment

Background:

  • Existing risk assessment methods lack a holistic approach for continuous, quantitative cyber risk analysis in IoT-based smart grids.
  • Attacks and defenses in smart grids can target diverse system components, complicating security investment decisions.

Purpose of the Study:

  • To propose a comprehensive methodology for continuous and quantitative cyber risk assessment in IoT-based smart grid systems.
  • To enable informed decisions for security protection and optimize security strategies through risk minimization.

Main Methods:

  • Utilizes attack-defense trees to model the smart grid system.
  • Computes risk attributes and propagates them through tree nodes for system risk assessment.
  • Incorporates standard security and privacy defense taxonomies (e.g., NIST SP 800-53).

Main Results:

  • The methodology allows for sensitivity analyses across various attack and defense scenarios.
  • Demonstrates the feasibility of initial quantitative risk estimation and continuous updates based on operational conditions.
  • Validated in a real-world smart building energy efficiency application.

Conclusions:

  • The proposed methodology provides a holistic and continuous approach to cyber risk assessment for smart grids.
  • Facilitates optimized security strategies and supports security certifications.
  • Enhances decision-making for security investments in complex IoT environments.