Blockchain Integration with Machine Learning for Securing Fog Computing Vulnerability in Smart City Sustainability

Lukman Adewale Ajao, S. T. Apeh
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引用次数: 1

Abstract

The advent of a smart city-based industrial Internet of Things (IIoT) is confidently built on the combined protocols of a virtual IPv6 addressing scheme and the fifth generation (5G) mobile network. For better network service and to achieve Quality of Experience (QoE) in the architecture. But this intelligent city architecture is vulnerable to several cyber-attack and malicious actors at the different layers which make it exposed to the same attacks as in the conventional IPv4 wireless sensor networks. However, this work aims to develop a blockchain-based machine learning (BML) security framework that secures the fog computing layer vulnerability in the smart city’s sustainability. The machine learning approach is firstly implemented between the edge layer and fog server nodes of the city architecture for the variants of intrusion detection using different ML algorithms for the attack’s discovery and classification. While the augmented blockchain technology is implemented between the fog layer and cloud computing to enhance the privacy and confidentiality of packet traffic broadcast to the public. The results obtained from ML-IDS show high-performance detection accuracy and low processing time. While the blockchain framework is also evaluated based on the certmcate generation, and retrieval size in bytes and time in milliseconds.
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区块链与机器学习的集成,以确保智慧城市可持续发展中的雾计算漏洞
基于智慧城市的工业物联网(IIoT)的出现是建立在虚拟IPv6寻址方案和第五代(5G)移动网络的组合协议之上的。为了更好的网络服务和实现体系结构中的体验质量(QoE)。但是,这种智能城市架构容易受到不同层的网络攻击和恶意行为者的攻击,这使得它暴露在与传统IPv4无线传感器网络相同的攻击中。然而,这项工作旨在开发基于区块链的机器学习(BML)安全框架,以确保智慧城市可持续性中的雾计算层漏洞。首先在城市架构的边缘层和雾服务器节点之间实现机器学习方法,使用不同的机器学习算法对入侵检测的变体进行攻击的发现和分类。而增强区块链技术则在雾层和云计算之间实现,以增强向公众广播的数据包流量的隐私性和保密性。结果表明,ML-IDS检测精度高,处理时间短。而区块链框架也是基于证书生成、检索大小(以字节为单位)和时间(以毫秒为单位)进行评估的。
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