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Automotive Software Abnormal Detection Method Based on Deep Learning.

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Title: Automotive Software Abnormal Detection Method Based on Deep Learning.
Authors: Zhao, Qiujun1 (AUTHOR), Tian, Ziwei1 (AUTHOR) tianziwei@catarc.ac.cn, Ju, Weinan1 (AUTHOR), Zhou, Shuhua1 (AUTHOR), Su, Yu1 (AUTHOR)
Source: International Journal of Automotive Technology. Apr2026, Vol. 27 Issue 2, p625-634. 10p.
Subjects: Automobile software, Deep learning, Recurrent neural networks, Software reliability, Telemetry, Graph neural networks, Service-oriented architecture (Computer science), Outlier detection
Abstract: With the rapid development of automotive intelligence and networking, the complexity and dynamism of automotive software systems have significantly increased, placing higher demands on software reliability. This paper proposes a new method for automotive software abnormal detection that integrates service grid and deep learning (DL) to effectively address the problems of high-dimensional, strongly correlated, and nonlinear behavior patterns in traditional abnormal detection methods. This method constructs a lightweight automotive service grid architecture, achieving transparent monitoring and flexible management of communication between services, providing comprehensive and multi-dimensional data support for abnormal detection. At the service grid control level, a fusion model of graph convolutional network (GCN) and recurrent neural network (RNN) is used for features extraction, and an end-to-end DL model is deployed to capture and deeply analyze telemetry data in real time, quickly identify, and effectively respond to various abnormal behaviors. The experimental results show that this method has achieved an accuracy rate of over 90% in abnormal detection of performance, safety, functionality, configuration, and compatibility, offering an effective means to ensure software reliability in the automotive intelligence and connectivity era. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Automotive Technology is the property of Springer Nature 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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IllustrationInfo
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  Data: Automotive Software Abnormal Detection Method Based on Deep Learning.
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  Data: <searchLink fieldCode="DE" term="%22Automobile+software%22">Automobile software</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Recurrent+neural+networks%22">Recurrent neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Software+reliability%22">Software reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Telemetry%22">Telemetry</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+neural+networks%22">Graph neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Service-oriented+architecture+%28Computer+science%29%22">Service-oriented architecture (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Outlier+detection%22">Outlier detection</searchLink>
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  Data: With the rapid development of automotive intelligence and networking, the complexity and dynamism of automotive software systems have significantly increased, placing higher demands on software reliability. This paper proposes a new method for automotive software abnormal detection that integrates service grid and deep learning (DL) to effectively address the problems of high-dimensional, strongly correlated, and nonlinear behavior patterns in traditional abnormal detection methods. This method constructs a lightweight automotive service grid architecture, achieving transparent monitoring and flexible management of communication between services, providing comprehensive and multi-dimensional data support for abnormal detection. At the service grid control level, a fusion model of graph convolutional network (GCN) and recurrent neural network (RNN) is used for features extraction, and an end-to-end DL model is deployed to capture and deeply analyze telemetry data in real time, quickly identify, and effectively respond to various abnormal behaviors. The experimental results show that this method has achieved an accuracy rate of over 90% in abnormal detection of performance, safety, functionality, configuration, and compatibility, offering an effective means to ensure software reliability in the automotive intelligence and connectivity era. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Automotive Technology is the property of Springer Nature 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: 10.1007/s12239-025-00281-1
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        Text: English
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        StartPage: 625
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      – SubjectFull: Automobile software
        Type: general
      – SubjectFull: Deep learning
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      – SubjectFull: Recurrent neural networks
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      – SubjectFull: Software reliability
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      – SubjectFull: Telemetry
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      – SubjectFull: Graph neural networks
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      – SubjectFull: Service-oriented architecture (Computer science)
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      – SubjectFull: Outlier detection
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      – TitleFull: Automotive Software Abnormal Detection Method Based on Deep Learning.
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            NameFull: Zhao, Qiujun
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            NameFull: Tian, Ziwei
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            NameFull: Ju, Weinan
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            NameFull: Zhou, Shuhua
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              M: 04
              Text: Apr2026
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              Y: 2026
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