Evidence map›Paper›PMID 42072558›Full record

ArticleEntropy (Basel, Switzerland)2026

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

Li Peng, Jiahai Tu, Siyuan Cai, Deng Chen

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Li PengSchool of Artificial Intelligence, Hubei Open University, Wuhan 430074, China.
Jiahai TuSchool of Artificial Intelligence, Hubei Open University, Wuhan 430074, China.
Siyuan CaiHubei Province Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan 430205, China.
Deng ChenHubei Province Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan 430205, China.ORCID 0000-0001-6359-801X

Funding

2024 Hubei Provincial Educational Planning Project 2024GB2802024 Innovation Fund Project of the Engineering Research Center of the Ministry of Education 1411007Scientific Research Program Project of Hubei Provincial Department of Education B2024288
6 · The paper itself

Abstract

With the interconnection and intercommunication of Internet of Things (IoT) devices, the security risks of Power Distribution Internet of Things (PDIoT) systems have increased accordingly. How to monitor and assess these risks has become a key issue for advancing the construction and implementation of PDIoT. The traditional security evaluation methods mostly adopt a single weighting method and membership function, which are highly susceptible to subjective factors and have the characteristics of fuzziness and uncertainty. To address the problems, this paper proposes a security risk evaluation method of the PDIoT based on a combined weighting and cloud model. We first analyze the factors of security risks in PDIoT systems. A security evaluation index system for PDIoT was established based on factors from three aspects: the perception layer, network layer, and application layer, including 3 first-level indicators and 16 second-level indicators. Then, the analytic hierarchy process (AHP) and entropy weight method (EWM) are adopted for combined weighting to optimize the weight of each index; the cloud model is employed to calculate the standard evaluation and the comprehensive evaluation cloud. Subsequently, validity verification and cloud similarity calculation are performed to get the security state of the PDIoT. Finally the security evaluation level of the PDIoT system is obtained. An empirical test was conducted by taking the PDIoT of Meizhou Power Supply Bureau of Guangdong Power Grid as an example. The experimental results show that our method has better result distinguishability than the other three classical methods, allowing risk levels to be identified more clearly and intuitively.

Indexed as

analytic hierarchy processcloud modelcombined weightingentropy weight methodpower distribution internet of things network securitysecurity risk evaluationthe comprehensive evaluation cloud

Identifiers

PMID42072558
PMCPMC13115220

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