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Real-Time Driver Behaviour Characterization Through Rule-Based Machine Learning

  • Fabio Martinelli
  • Francesco MercaldoEmail author
  • Vittoria Nardone
  • Antonella Santone
  • Gigliola Vaglini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11094)

Abstract

Modern car-embedded technologies enabled car thieves to perform new ways to steal cars. In order to avoid auto-theft attacks, in this paper we propose a machine learning based method to silently and continuously profile the driver by analyzing built-in vehicle sensors. The proposed method exploits rule-based machine learning with the aim to discriminate between the car owner and impostors. Furthermore, we discuss how the rules generated by the rule-based algorithm can be adopted in order to discriminate between different driving styles.

Keywords

Automotive Privacy Machine learning Authentication 

Notes

Acknowledgment

This work has been partially supported by H2020 EU-funded projects NeCS and C3ISP and EIT-Digital Project HII and PRIN “Governing Adaptive and Unplanned Systems of Systems” and the EU project CyberSure 734815.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Fabio Martinelli
    • 1
  • Francesco Mercaldo
    • 1
    Email author
  • Vittoria Nardone
    • 2
  • Antonella Santone
    • 3
  • Gigliola Vaglini
    • 4
  1. 1.Institute for Informatics and TelematicsNational Research Council of Italy (CNR)PisaItaly
  2. 2.Department of EngineeringUniversity of SannioBeneventoItaly
  3. 3.Department of Bioscience and TerritoryUniversity of MolisePescheItaly
  4. 4.Department of Information EngineeringUniversity of PisaPisaItaly

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