Security and Privacy in 5G-IIoT Smart Factories: Novel Approaches, Trends, and Challenges

Chun Cheng Lin, Ching Tsorng Tsai, Yu Liang Liu*, Tsai Ting Chang, Yung Sheng Chang

*此作品的通信作者

研究成果: Article同行評審

2 引文 斯高帕斯(Scopus)

摘要

To implement various artificial intelligence and automation applications in smart factories, edge computing and industrial Internet of Things (IIoT) devices must be widely deployed, so as to increase the demand of coping with huge-scale and high-diversity data. Through deployment of fifth-generation (5G) networks (providing wide broadband, low latency, and massive machine type communications), industrial wireless networks, cloud, and fixed/mobile end devices in smart factories are interoperated in a harmony. However, with the huge-scale deployment of 5G networks and the IIoT in smart factories, threats and attacks against various vulnerabilities increase enormously, and cause considerable security and privacy challenges. Consequently, this article investigates crucial security and privacy issues for 5G-IIoT smart factories in three entities (i.e., physical layer, data layer and application layer), and further surveys recent approaches based on deep learning, reinforcement learning, and blockchain. In addition, this article provides future perspectives and challenges along this line of research.

原文English
期刊Mobile Networks and Applications
DOIs
出版狀態Accepted/In press - 2023

指紋

深入研究「Security and Privacy in 5G-IIoT Smart Factories: Novel Approaches, Trends, and Challenges」主題。共同形成了獨特的指紋。

引用此