Good Vibrations : Artificial Ambience-Based Relay Attack Detection. / Gurulian, Iakovos; Markantonakis, Konstantinos; Frank, Eibe; Akram, Raja Naeem.

2018. Paper presented at The 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications, New York, United States.

Research output: Contribution to conferencePaper

E-pub ahead of print

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Abstract

Relay attacks are passive man in the middle attacks, aiming to extend the physical distance of devices involved in a transaction beyond their operating environment, within the restricted time-frame. In the field of smartphones, proposals have been put forward suggesting sensing the natural ambient environment as an effective Proximity and Relay Attack Detection (PRAD) mechanism. However, these proposals are not in compliance with industry imposed constraints (e.g.\ EMV and ITSO) mandating that transactions should complete within a certain time-frame (e.g.\ 500ms for EMV contactless transactions). The generation of an artificial ambient environment (AAE) using peripherals of the transaction devices has shown positive results when using infrared light as an AAE actuator. In this paper we propose the use of vibration as an alternative AAE actuator. We empirically evaluated the effectiveness of the proposed solution as a PRAD mechanism on an experimental test-bed that we deployed. A total of 36,000 genuine and relay attack transaction pairs were analysed using well-known machine learning algorithms. The results of our analysis indicate that the proposed solution is highly effective.
Original languageEnglish
Number of pages9
DOIs
StateE-pub ahead of print - 6 Sep 2018
EventThe 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications - New York, United States

Conference

ConferenceThe 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications
CountryUnited States
CityNew York
Period31/07/183/08/18
Internet address
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 29979044