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Towards End-to-End Data Protection in Low-Power Networks

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Computer Security (SECPRE 2017, CyberICPS 2017)

Abstract

An important, emerging trend in the context of the Internet of Things (IoT) are low-power networks (LPNs), referring to networks that target devices with very limited access to energy sources. While there are several approaches that allow to comply to these novel power restrictions, none of them provide a sufficient level of security, in particular with respect to data protection.

In this paper, we propose a data protection scheme that ensures end-to-end security from low-power devices to backend applications. It meets the technical constraints imposed by LPNs, while preserving data confidentiality and integrity. Our solution has been deployed on the water distribution network of the City of Antibes in France. The evaluation of the overhead introduced by the proposed data protection scheme shows promising results with respect to power (battery) consumption.

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Notes

  1. 1.

    This is required to adapt the packet size to possible length restrictions.

  2. 2.

    Resetting the device may be easily done by an attacker, e.g., by interrupting the power supply or even by using a hard-reset button, which is available/accessible on most devices.

  3. 3.

    See http://zephyr-docs.s3-website-us-east-1.amazonaws.com/online/dev/crypto/tinycrypt.html.

  4. 4.

    See http://www.intel.com/content/www/us/en/embedded/products/quark/mcu/se-soc/overview.html.

  5. 5.

    See https://www.zephyrproject.org/.

  6. 6.

    See https://cloudplatform.sap.com/.

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Acknowledgement

Mr. Patrick Duverger, CIO for the French Government in the City of Antibes, as well as Dr. Steffen Schulz, Intel Collaborative Research Institute for Secure Computing; whose insights and expertise greatly enriched this scientific work.

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Correspondence to Vasily Mikhalev .

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Mikhalev, V., Gomez, L., Armknecht, F., Márquez, J. (2018). Towards End-to-End Data Protection in Low-Power Networks. In: Katsikas, S., et al. Computer Security. SECPRE CyberICPS 2017 2017. Lecture Notes in Computer Science(), vol 10683. Springer, Cham. https://doi.org/10.1007/978-3-319-72817-9_1

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  • DOI: https://doi.org/10.1007/978-3-319-72817-9_1

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  • Online ISBN: 978-3-319-72817-9

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