Boussad, Y. orcid.org/0000-0001-9690-8221, Yang, Y. orcid.org/0000-0002-7970-2544, Tomlinson, A. et al. (1 more author) (2024) McMatcher: A symbolic representation for matching random BLE MAC addresses. In: 2024 IEEE International Conference on Consumer Electronics (ICCE). 2024 IEEE International Conference on Consumer Electronics (ICCE), 06-08 Jan 2024, Las Vegas, NV, USA. IEEE ISBN 979-8-3503-2414-3
Abstract
Bluetooth Low Energy (BLE) is a widely used wireless technology which offers a wide range of applications. However, the introduction of MAC address randomization to preserve the users' privacy makes it more challenging to leverage all its potential. In this paper, we present McMatcher, a privacy-preserving, novel methodology for matching random MAC addresses from BLE devices. McMatcher uses a symbolic representation of the RSSI time series to build characterizing vectors, embedding both the temporal as well as the signal strength (RSSI) properties of the BLE signal. Our methodology achieves 100% accuracy in matching 92 MAC addresses from 16 smartphones, in a dataset containing 332 MAC addresses in total. As opposed to previous works, our methodology does not require any model training, and relies only on the RSSI measurements. The computational simplicity of McMatcher allows matching MAC addresses in realtime, taking only 230ms for a set of 18 MAC addresses.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Training, Wireless communication, Measurement, Privacy, Protocols, Transmitters, Time series analysis, Tail, Vectors, Smart phones, Bluetooth Low Energy, MAC address randomization, RSSI, IoT, security and privacy, signal processing |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Sustainable Transport Policy (Leeds) The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Economics and Discrete Choice (Leeds) |
Funding Information: | Funder Grant number Innovate UK fka Technology Strategy Board (TSB) TS/V015788/1 EPSRC (Engineering and Physical Sciences Research Council) EP/V032658/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 14 Jun 2024 08:17 |
Last Modified: | 05 Jul 2024 12:06 |
Published Version: | https://ieeexplore.ieee.org/document/10444395 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/icce59016.2024.10444395 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:213511 |