Noncooperative 802.11 MAC Layer Fingerprinting and Tracking of Mobile Devices

We present two novel noncooperative MAC layer fingerprinting and tracking techniques for Wi-Fi (802.11) enabled mobile devices. Our first technique demonstrates how a per-bit entropy analysis of a single captured frame allows an adversary to construct a fingerprint of the transmitter that is 80.0 to...

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Main Authors: Pieter Robyns, Bram Bonné, Peter Quax, Wim Lamotte
Format: Article
Language:English
Published: Hindawi-Wiley 2017-01-01
Series:Security and Communication Networks
Online Access:http://dx.doi.org/10.1155/2017/6235484
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spelling doaj-5230addfb4304f749c916a0f226c22d92020-11-24T21:26:41ZengHindawi-WileySecurity and Communication Networks1939-01141939-01222017-01-01201710.1155/2017/62354846235484Noncooperative 802.11 MAC Layer Fingerprinting and Tracking of Mobile DevicesPieter Robyns0Bram Bonné1Peter Quax2Wim Lamotte3Expertise Centre for Digital Media, UHasselt-tUL-imec, Wetenschapspark 2, 3590 Diepenbeek, BelgiumExpertise Centre for Digital Media, UHasselt-tUL-imec, Wetenschapspark 2, 3590 Diepenbeek, BelgiumExpertise Centre for Digital Media, UHasselt-tUL-imec, Wetenschapspark 2, 3590 Diepenbeek, BelgiumExpertise Centre for Digital Media, UHasselt-tUL-imec, Wetenschapspark 2, 3590 Diepenbeek, BelgiumWe present two novel noncooperative MAC layer fingerprinting and tracking techniques for Wi-Fi (802.11) enabled mobile devices. Our first technique demonstrates how a per-bit entropy analysis of a single captured frame allows an adversary to construct a fingerprint of the transmitter that is 80.0 to 67.6 percent unique for 50 to 100 observed devices and 33.0 to 15.1 percent unique for 1,000 to 10,000 observed devices. We show how existing mitigation strategies such as MAC address randomization can be circumvented using only this fingerprint and temporal information. Our second technique leverages peer-to-peer 802.11u Generic Advertisement Service (GAS) requests and 802.11e Block Acknowledgement (BA) requests to instigate transmissions on demand from devices that support these protocols. We validate these techniques using two datasets, one of which was recorded at a music festival containing 28,048 unique devices and the other at our research lab containing 138 unique devices. Finally, we discuss a number of countermeasures that can be put in place by mobile device vendors in order to prevent noncooperative tracking through the discussed techniques.http://dx.doi.org/10.1155/2017/6235484
collection DOAJ
language English
format Article
sources DOAJ
author Pieter Robyns
Bram Bonné
Peter Quax
Wim Lamotte
spellingShingle Pieter Robyns
Bram Bonné
Peter Quax
Wim Lamotte
Noncooperative 802.11 MAC Layer Fingerprinting and Tracking of Mobile Devices
Security and Communication Networks
author_facet Pieter Robyns
Bram Bonné
Peter Quax
Wim Lamotte
author_sort Pieter Robyns
title Noncooperative 802.11 MAC Layer Fingerprinting and Tracking of Mobile Devices
title_short Noncooperative 802.11 MAC Layer Fingerprinting and Tracking of Mobile Devices
title_full Noncooperative 802.11 MAC Layer Fingerprinting and Tracking of Mobile Devices
title_fullStr Noncooperative 802.11 MAC Layer Fingerprinting and Tracking of Mobile Devices
title_full_unstemmed Noncooperative 802.11 MAC Layer Fingerprinting and Tracking of Mobile Devices
title_sort noncooperative 802.11 mac layer fingerprinting and tracking of mobile devices
publisher Hindawi-Wiley
series Security and Communication Networks
issn 1939-0114
1939-0122
publishDate 2017-01-01
description We present two novel noncooperative MAC layer fingerprinting and tracking techniques for Wi-Fi (802.11) enabled mobile devices. Our first technique demonstrates how a per-bit entropy analysis of a single captured frame allows an adversary to construct a fingerprint of the transmitter that is 80.0 to 67.6 percent unique for 50 to 100 observed devices and 33.0 to 15.1 percent unique for 1,000 to 10,000 observed devices. We show how existing mitigation strategies such as MAC address randomization can be circumvented using only this fingerprint and temporal information. Our second technique leverages peer-to-peer 802.11u Generic Advertisement Service (GAS) requests and 802.11e Block Acknowledgement (BA) requests to instigate transmissions on demand from devices that support these protocols. We validate these techniques using two datasets, one of which was recorded at a music festival containing 28,048 unique devices and the other at our research lab containing 138 unique devices. Finally, we discuss a number of countermeasures that can be put in place by mobile device vendors in order to prevent noncooperative tracking through the discussed techniques.
url http://dx.doi.org/10.1155/2017/6235484
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