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The increasing integration of Internet of Things (IoT) technology in the automotive industry has paved the way for innovative solutions to enhance vehicle safety. This study examines how the Internet of Vehicles (IoV) and machine learning (ML) might be creatively used to improve mobility in the face of increasing vehicle safety problems. This paper explores the intersection of Internet of Vehicles (IOV) and Machine Learning (ML) techniques for anomaly detection, focusing specifically on the prevention and detection of stolen vehicle incidents. The combination of IoV and ML represents a potent synergy that uses real-time data and predictive analytics to provide a strong security framework for the changing terrain of contemporary transportation. The research investigates the deployment of connected vehicles equipped with sensors and communication modules, forming a dynamic network that continuously exchanges data with the surrounding environmentTo improve anomaly detection, we suggest combining machine learning with law enforcement databases that contain stolen automobile information. The ability to quickly identify and report stolen automobiles through real-time cross-referencing facilitates prompt response and recovery operations. This multidisciplinary research combines IoT tools, machine learningMachine learning (ML), computer vision, and data analytics to develop effective anomaly detection systems, offering insights into enhancing vehicle securitySecurities across diverse contexts. These models not only detect unauthorized access and potential theft in real time but also adapt and evolve through continuous learning from historical and emerging data patterns. The study reviews existing methodologies and technologies in the field, highlighting the strengths and limitations of current systems. It further explores the challenges associated with securing mobility, such as the need for standardized communication protocolsCommunication protocols, privacyPrivacies concerns, and the potential vulnerabilities of interconnected systems. The paper also discusses the ethical considerations in implementing machine learningMachine learning (ML) algorithms for securitySecurities purposes, emphasizing the importance of responsible AIArtificial intelligence(AI) practices. This research contributes to the ongoing discourse on securing mobility through the integration of the Internet of VehiclesInternet of vehicles (IoV) and Machine LearningMachine learning (ML). By addressing the technical, ethical, and practical aspects of anomaly detection in stolen vehicle incidents, this study provides valuable insights for researchers, practitioners, and policymakers working toward enhancing the safety and securitySecurities of the future transportation ecosystem.

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Guardians of the Road: Harnessing Internet of Vehicles and Machine Learning for Combating Vehicle Theft Through Anomaly Detection

  • 章节

  • 发表日期：

    2026年

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  • Pradipta Pal,
  • Pabitra Kumar Dey,
  • Abhoy Chand Mondal,
  • Satyajit Rath

摘要

The increasing integration of Internet of Things (IoT) technology in the automotive industry has paved the way for innovative solutions to enhance vehicle safety. This study examines how the Internet of Vehicles (IoV) and machine learning (ML) might be creatively used to improve mobility in the face of increasing vehicle safety problems. This paper explores the intersection of Internet of Vehicles (IOV) and Machine Learning (ML) techniques for anomaly detection, focusing specifically on the prevention and detection of stolen vehicle incidents. The combination of IoV and ML represents a potent synergy that uses real-time data and predictive analytics to provide a strong security framework for the changing terrain of contemporary transportation. The research investigates the deployment of connected vehicles equipped with sensors and communication modules, forming a dynamic network that continuously exchanges data with the surrounding environmentTo improve anomaly detection, we suggest combining machine learning with law enforcement databases that contain stolen automobile information. The ability to quickly identify and report stolen automobiles through real-time cross-referencing facilitates prompt response and recovery operations. This multidisciplinary research combines IoT tools, machine learningMachine learning (ML), computer vision, and data analytics to develop effective anomaly detection systems, offering insights into enhancing vehicle securitySecurities across diverse contexts. These models not only detect unauthorized access and potential theft in real time but also adapt and evolve through continuous learning from historical and emerging data patterns. The study reviews existing methodologies and technologies in the field, highlighting the strengths and limitations of current systems. It further explores the challenges associated with securing mobility, such as the need for standardized communication protocolsCommunication protocols, privacyPrivacies concerns, and the potential vulnerabilities of interconnected systems. The paper also discusses the ethical considerations in implementing machine learningMachine learning (ML) algorithms for securitySecurities purposes, emphasizing the importance of responsible AIArtificial intelligence(AI) practices. This research contributes to the ongoing discourse on securing mobility through the integration of the Internet of VehiclesInternet of vehicles (IoV) and Machine LearningMachine learning (ML). By addressing the technical, ethical, and practical aspects of anomaly detection in stolen vehicle incidents, this study provides valuable insights for researchers, practitioners, and policymakers working toward enhancing the safety and securitySecurities of the future transportation ecosystem.