Deep Learning and Blockchain-Empowered Security Framework for Intelligent 5G-Enabled IoT

【Author】 Rathore, Shailendra; Park, Jong Hyuk; Chang, Hangbae

【Source】IEEE ACCESS

【影响因子】3.476

【Abstract】Recently, many IoT applications, such as smart transportation, healthcare, and virtual and augmented reality experiences, have emerged with fifth-generation (5G) technology to enhance the Quality of Service (QoS) and user experience. The revolution of 5G-enabled IoT supports distinct attributes, including lower latency, higher system capacity, high data rate, and energy saving. However, such revolution also delivers considerable increment in data generation that further leads to a major requirement of intelligent and effective data analytic operation across the network. Furthermore, data growth gives rise to data security and privacy concerns, such as breach and loss of sensitive data. The conventional data analytic and security methods do not meet the requirement of 5G-enabled IoT including its unique characteristic of low latency and high throughput. In this paper, we propose a Deep Learning (DL) and blockchain-empowered security framework for intelligent 5G-enabled IoT that leverages DL competency for intelligent data analysis operation and blockchain for data security. The framework's hierarchical architecture wherein DL and blockchain operations emerge across the four layers of cloud, fog, edge, and user is presented. The framework is simulated and analyzed, employing various standard measures of latency, accuracy, and security to demonstrate its validity in practical applications.

【Keywords】Security; Internet of Things; Data analysis; Blockchain; Reliability; 5G mobile communication; Quality of service; Internet of Things; security attack detection; edge computing; fog computing; blockchain; software-defined networking

【发表时间】2021

【收录时间】2022-01-02

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