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

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Computer Safety, Reliability, and Security (SAFECOMP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11094))

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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.

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Notes

  1. 1.

    https://www.ic3.gov/media/2016/160317.aspx.

  2. 2.

    https://www.iii.org/fact-statistic/facts-statistics-auto-theft.

  3. 3.

    https://www.nicb.org/.

  4. 4.

    http://www.totalcardiagnostics.com/elm327-bluetooth/.

  5. 5.

    http://www.cs.waikato.ac.nz/ml/weka/.

  6. 6.

    https://play.google.com/store/apps/details?id=org.prowl.torquefree.

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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.

Author information

Authors and Affiliations

  1. Institute for Informatics and Telematics, National Research Council of Italy (CNR), Pisa, Italy

    Fabio Martinelli & Francesco Mercaldo

  2. Department of Engineering, University of Sannio, Benevento, Italy

    Vittoria Nardone

  3. Department of Bioscience and Territory, University of Molise, Pesche, IS, Italy

    Antonella Santone

  4. Department of Information Engineering, University of Pisa, Pisa, Italy

    Gigliola Vaglini

Authors
  1. Fabio Martinelli
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  2. Francesco Mercaldo
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  3. Vittoria Nardone
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  4. Antonella Santone
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  5. Gigliola Vaglini
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Corresponding author

Correspondence to Francesco Mercaldo .

Editor information

Editors and Affiliations

  1. Mälardalen University, Västerås, Sweden

    Barbara Gallina

  2. Norwegian University of Science and Technology, Trondheim, Norway

    Amund Skavhaug

  3. AIT Austrian Institute of Technology, Vienna, Austria

    Erwin Schoitsch

  4. Thales Deutschland GmbH, Ditzingen, Germany

    Friedemann Bitsch

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Martinelli, F., Mercaldo, F., Nardone, V., Santone, A., Vaglini, G. (2018). Real-Time Driver Behaviour Characterization Through Rule-Based Machine Learning. In: Gallina, B., Skavhaug, A., Schoitsch, E., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2018. Lecture Notes in Computer Science(), vol 11094. Springer, Cham. https://doi.org/10.1007/978-3-319-99229-7_32

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  • DOI: https://doi.org/10.1007/978-3-319-99229-7_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99228-0

  • Online ISBN: 978-3-319-99229-7

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