Security Risk Intelligent Assessment of Power Distribution Internet of Things via Entropy-Weight Method and Cloud Model

Abstract

The current power distribution Internet of Things (PDIoT) lacks security protection terminals and techniques. Network security has a large exposure surface that can be attacked from multiple paths. In addition, there are many network security vulnerabilities and weak security protection capabilities of power distribution Internet of Things terminals. Therefore, it is crucial to conduct a scientific assessment of the security of PDIoT. However, traditional security assessment methods are relatively subjective and ambiguous. To address the problems, we propose to use the entropy-weight method and cloud model theory to assess the security risk of the PDIoT. We first analyze the factors of security risks in PDIoT systems and establish a three-layer PDIoT security evaluation index system, including a perception layer, network layer, and application layer. The index system has three first-level indicators and sixteen second-level indicators. Then, the entropy-weight method is used to optimize the weight of each index. Additionally, the cloud model theory is employed to calculate the affiliation degree and eigenvalue of each evaluation index. Based on a comprehensive analysis of all evaluation indexes, we can achieve the security level of PDIoT. Taking the PDIoT of Meizhou Power Supply Bureau of Guangdong Power Grid as an example for empirical testing, the experimental results show that the evaluation results are consistent with the actual situation, which proves that the proposed method is effective and feasible.

AHP-Based Network Security Situation Assessment for Industrial Internet of Things

“…The model considered the network's deterrence against threats, resilience against attacks, and ability to withstand shocks, and accurately measured the security effectiveness of the entire network and its context components. Cai et al [17] established a three-layer distribution Internet of Things (PDIoT) security evaluation index system and used the entropy weight method and cloud model theory to evaluate the security risks of PDIoT. Venkataramanan et al [18] proposed a model for detecting the resistance of microgrids to attacks.…”

“…The threat severity is determined by the attack probability within a time, as shown in (17). If the data in clause i is normal traffic, I i is marked with 0; otherwise, it is marked with 1; M represents the total network traffic within a time.…”

A novel network security situation assessment model based on multiple strategies whale optimization algorithm and bidirectional GRU

“…By referring to the existing indicator system and combined with the existing index selection methods ( Feng-zhu et al, 2015 ; Cai et al, 2022 ), the network security situation can be divided into four first-class indexes from different angles, namely, the vulnerability index, threat index, reliability index, and availability index. Among these, the reliability and availability indexes can be subdivided into multiple second-class indexes.…”

Multi-Risk Assessment of Mine Lithium Battery Fire Based on Quantitative Factor Characterization

“…Although FAHP has obvious advantages, this method still requires subjective judgment from experts, the scoring matrix may have logical errors or omissions, and there would be deviations from human thinking when checking the consistency of machine operations. At this time, A quantitative model presented in Section 3 was introduced to calculate partial risk factors in the FAHP model and optimize the indexes weight calculation, so as to reduce errors [24]. In order to obtain quantitative risk indicators scientifically and objectively, this paper proposed to establish risk failure probability data by means of lithium battery thermal runaway test and mine external cause fire simulation.…”