Power Distribution Internet of Things Security Risk Evaluation Based on Combined Weighting and Cloud Model.

Area of Science:

• Electrical Engineering
• Computer Science
• Information Security

Background:

• The increasing interconnectedness of Internet of Things (IoT) devices elevates security risks in Power Distribution Internet of Things (PDIoT) systems.
• Traditional risk assessment methods for PDIoT are often subjective and lack precision due to fuzzy logic and uncertainty.
• Effective monitoring and assessment of PDIoT security risks are crucial for system development and implementation.

Purpose of the Study:

• To propose an advanced security risk evaluation method for PDIoT systems.
• To address the limitations of traditional methods by incorporating combined weighting and cloud models.
• To enhance the accuracy and objectivity of PDIoT security risk assessment.

Main Methods:

• Development of a PDIoT security evaluation index system with 3 first-level and 16 second-level indicators across perception, network, and application layers.
• Application of combined weighting using Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) to optimize index weights.
• Utilization of the cloud model for calculating standard and comprehensive evaluation clouds, followed by validity verification and cloud similarity calculations.

Main Results:

• The proposed method establishes a comprehensive security evaluation index system for PDIoT.
• Combined weighting optimizes index importance, while the cloud model quantifies security states.
• Empirical testing on a real-world PDIoT system demonstrated superior result distinguishability compared to classical methods.

Conclusions:

• The novel security risk evaluation method provides a more objective and precise assessment of PDIoT systems.
• The combined weighting and cloud model approach enhances the clarity and intuitiveness of risk level identification.
• This method supports more effective construction and implementation of secure PDIoT infrastructure.