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      On the effectiveness of ambient sensing for detecting NFC Relay Attacks

      Gurulian, Iakovos; Shepherd, Carlton; Frank, Eibe; Markantonakis, Konstantinos; Akram, Raja Naeem; Mayes, Keith
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      accepted paper.pdf
      Accepted version, 470.2Kb
      DOI
       10.1109/Trustcom/BigDataSE/ICESS.2017.218
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      Gurulian, I., Shepherd, C., Frank, E., Markantonakis, K., Akram, R. N., & Mayes, K. (2017). On the effectiveness of ambient sensing for detecting NFC Relay Attacks. In Proceedings of 2017 IEEE Trustcom/BigDataSE/ICESS (pp. 41–49). Washington, DC, USA: IEEE. https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.218
      Permanent Research Commons link: https://hdl.handle.net/10289/12030
      Abstract
      Smartphones with Near-Field Communication (NFC) may emulate contactless smart cards, which has resulted in the deployment of various access control, transportation and payment services, such as Google Pay and Apple Pay. Like contactless cards, however, NFC-based smartphone transactions are susceptible to relay attacks, and ambient sensing has been suggested as a potential countermeasure. In this study, we empirically evaluate the suitability of ambient sensors as a proximity detection mechanism for smartphone-based transactions under EMV constraints. We underpin our study using sensing data collected from 17 sensors from an emulated relay attack test-bed to assess whether they can thwart such attacks effectively. Each sensor, where feasible, was used to record 350-400 legitimate and relay (illegitimate) contactless transactions at two different physical locations. Our analysis provides an empirical foundation upon which to determine the efficacy of ambient sensing for providing a strong anti-relay mechanism in security-sensitive applications. We demonstrate that no single, evaluated mobile ambient sensor is suitable for such critical applications under realistic deployment constraints.
      Date
      2017
      Type
      Conference Contribution
      Publisher
      IEEE
      Rights
      This is an author’s accepted version of an article published in the Proceedings of 2017 IEEE Trustcom/BigDataSE/ICESS. © 2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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      • Computing and Mathematical Sciences Papers [1455]
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