Cloud-Based Secure Data Management for Internet of Vehicles Platforms.

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Bibliographic Details
Title:	Cloud-Based Secure Data Management for Internet of Vehicles Platforms.
Authors:	XIANG, Ke¹ xiangke@sptc.edu.cn, YANG, Xing² 18980091368@163.com, WANG, Huihui³ 17864808780@139.com
Source:	Technical Gazette / Tehnički Vjesnik. 2026, Vol. 33 Issue 3, p979-987. 9p.
Subjects:	Data security, Internet of things, Machine learning, Support vector machines, Database management, Cloud computing, Logistic regression analysis, Nonrelational databases
Abstract:	The rapid growth of Internet of Vehicles (IoV) platforms has raised significant concerns about data security and storage efficiency. This study proposes a novel approach that is integrating cloud computing, machine learning, and advanced database management to enhance IoV data security and storage. MongoDB is utilized for data storage. The logistic regression and Support Vector Machine (SVM) algorithms are combined for security detection. By this way, our method demonstrates improved performance over traditional approaches. Experimental results showed a 4.60% increase in data recognition precision, a 4.66% higher recall, and a 2.55% improvement in the F1 score compared to existing models. The proposed system also exhibits enhanced data storage efficiency and robust security detection capabilities. These findings demonstrate our method has significant potential for improving IoV platform security and data management in real-world applications. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	The rapid growth of Internet of Vehicles (IoV) platforms has raised significant concerns about data security and storage efficiency. This study proposes a novel approach that is integrating cloud computing, machine learning, and advanced database management to enhance IoV data security and storage. MongoDB is utilized for data storage. The logistic regression and Support Vector Machine (SVM) algorithms are combined for security detection. By this way, our method demonstrates improved performance over traditional approaches. Experimental results showed a 4.60% increase in data recognition precision, a 4.66% higher recall, and a 2.55% improvement in the F1 score compared to existing models. The proposed system also exhibits enhanced data storage efficiency and robust security detection capabilities. These findings demonstrate our method has significant potential for improving IoV platform security and data management in real-world applications. [ABSTRACT FROM AUTHOR]
ISSN:	13303651
DOI:	10.17559/TV-20240428001508