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

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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]
Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Cloud-Based Secure Data Management for Internet of Vehicles Platforms.
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  Data: <searchLink fieldCode="AR" term="%22XIANG%2C+Ke%22">XIANG, Ke</searchLink><relatesTo>1</relatesTo><i> xiangke@sptc.edu.cn</i><br /><searchLink fieldCode="AR" term="%22YANG%2C+Xing%22">YANG, Xing</searchLink><relatesTo>2</relatesTo><i> 18980091368@163.com</i><br /><searchLink fieldCode="AR" term="%22WANG%2C+Huihui%22">WANG, Huihui</searchLink><relatesTo>3</relatesTo><i> 17864808780@139.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Technical+Gazette+%2F+Tehnički+Vjesnik%22">Technical Gazette / Tehnički Vjesnik</searchLink>. 2026, Vol. 33 Issue 3, p979-987. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Data+security%22">Data security</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+of+things%22">Internet of things</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Database+management%22">Database management</searchLink><br /><searchLink fieldCode="DE" term="%22Cloud+computing%22">Cloud computing</searchLink><br /><searchLink fieldCode="DE" term="%22Logistic+regression+analysis%22">Logistic regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Nonrelational+databases%22">Nonrelational databases</searchLink>
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  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.17559/TV-20240428001508
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: Internet of things
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Support vector machines
        Type: general
      – SubjectFull: Database management
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      – SubjectFull: Cloud computing
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      – SubjectFull: Logistic regression analysis
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      – SubjectFull: Nonrelational databases
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      – TitleFull: Cloud-Based Secure Data Management for Internet of Vehicles Platforms.
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            NameFull: XIANG, Ke
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            NameFull: YANG, Xing
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            NameFull: WANG, Huihui
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            – D: 01
              M: 05
              Text: 2026
              Type: published
              Y: 2026
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