基于节点动态关联的电力物联网轻量级反共谋信任模型

IF 1.4 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Shice Zhao, Hongshan Zhao, Jingjie Sun
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引用次数: 0

摘要

电网中引入了大量的监测传感器。然而,通常用于边缘侧安全管理的传统信任模型在检测大规模恶意交互和共谋攻击方面较弱。为此,提出了一种结合节点动态相关性的轻量级、反共谋的电力物联网信任模型。首先,根据电网中传感器的工作机制,构建了一个全局信任管理系统。然后,将信任反馈和设备的接触频率相结合,建立了一个基于相关性波动性的自适应动态权重向量。减少了信任值的波动,扩大了正常节点和恶意节点之间的信任差异。为了有效地检测共谋攻击,还设计了一种基于接触集感知的反共谋算法。校验和本地广播是在信任模型中建立的,以抵消智能终端故障的风险。结果表明,在50个节点的网络规模中,当恶意节点的最大比例为20%时,信任模型的节点识别准确率达到100%。此外,整个模型的计算时间为211毫秒,内存消耗为161 kb,适用于电力物联网传感器网络。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A lightweight and anti-collusion trust model combined with nodes dynamic relevance for the power internet of things

A lightweight and anti-collusion trust model combined with nodes dynamic relevance for the power internet of things

A large number of monitoring sensors are introduced in the power grid. However, the traditional trust models commonly used for edge-side security management are weak in detecting large-scale malicious interactions and collusion attacks. For that, a lightweight and anti-collusion trust model combined with nodes’ dynamic relevance for the power Internet of Things (IoT) is proposed. Firstly, a global trust management system is constructed according to the working mechanism of sensors in the power grid. After that, trust feedback and contact frequency of the devices are combined to build an adaptive dynamic weight vector based on relevance volatility. Fluctuations in trust values are reduced and the trust difference between normal and malicious nodes is widened. An anti-collusion algorithm based on contact set awareness is also designed to effectively detect collusion attacks. The checksum local broadcast is established in the trust model to counteract the risk of intelligent terminal failure. The results show that the trust model achieves 100% accuracy of node discrimination when the maximum proportion of malicious nodes is 20% in a 50-node network scale. In addition, the calculation time of the overall model is 211 ms and the memory consumption is 161 kb, which is suitable for power IoT sensor networks.

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来源期刊
Iet Science Measurement & Technology
Iet Science Measurement & Technology 工程技术-工程:电子与电气
CiteScore
4.30
自引率
7.10%
发文量
41
审稿时长
7.5 months
期刊介绍: IET Science, Measurement & Technology publishes papers in science, engineering and technology underpinning electronic and electrical engineering, nanotechnology and medical instrumentation.The emphasis of the journal is on theory, simulation methodologies and measurement techniques. The major themes of the journal are: - electromagnetism including electromagnetic theory, computational electromagnetics and EMC - properties and applications of dielectric, magnetic, magneto-optic, piezoelectric materials down to the nanometre scale - measurement and instrumentation including sensors, actuators, medical instrumentation, fundamentals of measurement including measurement standards, uncertainty, dissemination and calibration Applications are welcome for illustrative purposes but the novelty and originality should focus on the proposed new methods.
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