Security, Trust, and Privacy for the Internet of Vehicles: A Deep Learning Approach

被引：15

作者：

Muhammad, Ghulam ^{[1
]}

Alhussein, Musaed ^{[2
]}

机构：

[1] King Saud Univ, Dept Comp Engn, Coll Comp & Informat Sci, Riyadh, Saudi Arabia

[2] King Saud Univ, Dept Comp Engn, Riyadh, Saudi Arabia

来源：

IEEE CONSUMER ELECTRONICS MAGAZINE | 2022年 / 11卷 / 06期

关键词：

Intelligent vehicles; Security; Authentication; Convolutional codes; Convolutional neural networks; Vehicle safety; Real-time systems; Internet of Vehicles; AUTHENTICATION;

D O I：

10.1109/MCE.2021.3089880

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Intelligent sensing plays an important part in making our use of vehicles safe and problem-free. On average, a person spends over 35 hours in traffic jams each year. This valuable time could be saved by intelligent routing and real-time traffic alerts. Transport is a necessity of life, both in our everyday lives and at work. Navigation apps are now enabling users to access real-time alerts and alternatives. However, with the increase in the number of Internet-of-Vehicle-Things (IoVT), a large amount of data is produced within a short period of time. The huge data produced by the IoVT could be used to obtain greater perspective and to make dramatically smarter decisions. With this data, there is always a risk to security, trust, and privacy (STP). A standardized protocol is needed to preserve privacy and maintain the security of data. This paper addressed several STP issues in an intelligent transportation system. In addition, a deep learning model is proposed to process data generated by the IoVT.

引用

页码：49 / 55

页数：7

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