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An Association Rules-Based Approach for Anomaly Detection on CAN-bus

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Computational Science and Its Applications – ICCSA 2023 Workshops (ICCSA 2023)

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Abstract

With the rapid development of the Internet of Things (IoT) and the Internet of Vehicles (IoV) technologies, smart vehicles have replaced conventional ones by providing more advanced driving-related features. IoV systems typically consist of Intra-Vehicle Networks (IVNs) in which many Electronic Control units (ECUs) directly and indirectly communicate among them through the Controller Area Network (CAN) bus. However, the growth of such vehicles has also increased the number of network and physical attacks focused on exploiting security weaknesses affecting the CAN protocol. Such problems can also endanger the life of the driver and passengers of the vehicle, as well as that of pedestrians. Therefore, to face this security issue, we propose a novel anomaly detector capable of considering the vehicle-related state over time. To accomplish this, we combine different most famous algorithms to consider all possible relationships between CAN messages and arrange them as corresponding associative rules. The presented approach, also compared with the state-of-the-art solutions, can effectively detect different kinds of attacks (DoS, Fuzzy, GEAR and RPM) by only considering CAN messages collected during attack-free operating scenarios.

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Notes

  1. 1.

    Dataset available at https://ocslab.hksecurity.net/Datasets/car-hacking-dataset.

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Acknowledgments

This work was partially supported by project SERICS (PE00000014) under the NRRP MUR program funded by the EU - NGEU.

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Authors and Affiliations

  1. Department of Computer Science, University of Salerno, Salerno, Italy

    Gianni D’Angelo, Massimo Ficco & Antonio Robustelli

Authors
  1. Gianni D’Angelo
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  2. Massimo Ficco
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  3. Antonio Robustelli
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Corresponding author

Correspondence to Antonio Robustelli .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Basilicata, Potenza, Italy

    Francesco Scorza

  6. University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

  7. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

Ethics declarations

Gianni D’Angelo

Supervision, Methodology, and Reviewing.

Massimo Ficco

Supervision, Methodology, and Reviewing.

Antonio Robustelli

Investigation, Conceptualization, and Writing.

Conflict of interest

The authors declare that they have no conflict of interest.

Availability of Data and Material

The used data is available on https://ocslab.hksecurity.net/Datasets/car-hacking-dataset.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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D’Angelo, G., Ficco, M., Robustelli, A. (2023). An Association Rules-Based Approach for Anomaly Detection on CAN-bus. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14105. Springer, Cham. https://doi.org/10.1007/978-3-031-37108-0_12

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  • DOI: https://doi.org/10.1007/978-3-031-37108-0_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37107-3

  • Online ISBN: 978-3-031-37108-0

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