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Machine Learning and Deep Learning for Hardware Fingerprinting

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Security and Artificial Intelligence

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13049))

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Abstract

Device or machine fingerprinting is the process of collecting information on a (part of a) device for its identification. This can be done under different scenarios and using information from different hardware and software layers of the device. Hardware fingerprinting typically refers to device fingerprinting using information collected from the hardware layer. Hardware fingerprinting can have nefarious usages related to privacy abuse, as well as many positive ones such as soft authentication, indoor positioning systems, and others. Here we introduce some of the uses of hardware fingerprinting, with special emphasis on those related to commonly available devices, and explain how machine learning and deep learning have enabled and/or improved them. Additionally, we discuss some of their limitations and possibilities for improvement.

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Notes

  1. 1.

    The MadWiFi driver, present in many Atheros chipsets.

  2. 2.

    Periodic CAN messages can typically constitute around \(90\%\) of CAN bus messages, although this varies by vehicle platform and operating conditions.

  3. 3.

    The CAN bus arbitrates priority based on the ID of the messages colliding. Using extended identifiers when there is no need, it is possible to fix 18 bits of the ID for all ECUs.

  4. 4.

    Note that ML and DL algorithms aim for this, but typically can only test the behaviour in a particular test dataset.

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  1. Complutense University, Madrid, Spain

    Carlos Javier Hernandez-Castro

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    Ileana Buhan

  4. Radboud University Nijmegen, Nijmegen, The Netherlands

    Stjepan Picek

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Hernandez-Castro, C.J. (2022). Machine Learning and Deep Learning for Hardware Fingerprinting. In: Batina, L., Bäck, T., Buhan, I., Picek, S. (eds) Security and Artificial Intelligence. Lecture Notes in Computer Science, vol 13049. Springer, Cham. https://doi.org/10.1007/978-3-030-98795-4_9

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