Linking wireless devices using information contained in Wi-Fi probe requests : Wireless Mobile and Multimedia Networks
Identifieur interne : 005B64 ( PascalFrancis/Curation ); précédent : 005B63; suivant : 005B65Linking wireless devices using information contained in Wi-Fi probe requests : Wireless Mobile and Multimedia Networks
Auteurs : Mathieu Cunche [France] ; Mohamed-Ali Kaafar [France, Australie] ; Roksana Boreli [Australie]Source :
- Pervasive and mobile computing : (Print) [ 1574-1192 ] ; 2014.
Descripteurs français
- Pascal (Inist)
- Wicri :
- geographic : Australie.
- topic : Classification, Confidentialité.
English descriptors
- KwdEn :
Abstract
Active service discovery in Wi-Fi involves wireless stations broadcasting their Wi-Fi fingerprint, i.e. the SSIDs of their preferred wireless networks. The content of those Wi-Fi fingerprints can reveal different types of information about the owner. We focus on the relation between the fingerprints and the links between the owners. Our hypothesis is that social links between devices' owners can be identified by exploiting the information contained in the fingerprint. More specifically we propose to consider the similarity between fingerprints as a metric, with the underlying idea: similar fingerprints are likely to be linked. We first study the performances of several similarity metrics on a controlled dataset and then apply the designed classifier to a dataset collected in the wild. Finally we discuss potential countermeasures and propose a new one based on geolocation. This study is based on a dataset collected in Sydney, Australia, composed of fingerprints belonging to more than 8000 devices.
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<term>Localization</term>
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<term>Pervasive computing</term>
<term>Private life</term>
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<front><div type="abstract" xml:lang="en">Active service discovery in Wi-Fi involves wireless stations broadcasting their Wi-Fi fingerprint, i.e. the SSIDs of their preferred wireless networks. The content of those Wi-Fi fingerprints can reveal different types of information about the owner. We focus on the relation between the fingerprints and the links between the owners. Our hypothesis is that social links between devices' owners can be identified by exploiting the information contained in the fingerprint. More specifically we propose to consider the similarity between fingerprints as a metric, with the underlying idea: similar fingerprints are likely to be linked. We first study the performances of several similarity metrics on a controlled dataset and then apply the designed classifier to a dataset collected in the wild. Finally we discuss potential countermeasures and propose a new one based on geolocation. This study is based on a dataset collected in Sydney, Australia, composed of fingerprints belonging to more than 8000 devices.</div>
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