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DEMO

Célestin Matte, Mathieu Cunche
<span title="">2016</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qseddxfnjracne6jxnlhvkmmlu" style="color: black;">Proceedings of the 9th ACM Conference on Security &amp; Privacy in Wireless and Mobile Networks - WiSec &#39;16</a> </i> &nbsp;
MAC address randomization [5] in Wi-Fi-enabled devices has recently been adopted to prevent passive tracking of mobile devices.  ...  Panoptiphone uses this information to create a fingerprint of the device and empirically evaluate its uniqueness among a database of fingerprints.  ...  We also aim to raise public concern on the trackability of devices carried by almost anyone at any time. WiSec'16 July 18-22, 2016, Darmstadt, Germany c 2016 Copyright held by the owner/author(s).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2939918.2942417">doi:10.1145/2939918.2942417</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/wisec/MatteC16.html">dblp:conf/wisec/MatteC16</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rd6fpnu5wvh27n3ru3dpqofzou">fatcat:rd6fpnu5wvh27n3ru3dpqofzou</a> </span>
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Smart-Phones Attacking Smart-Homes

Vijay Sivaraman, Dominic Chan, Dylan Earl, Roksana Boreli
<span title="">2016</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qseddxfnjracne6jxnlhvkmmlu" style="color: black;">Proceedings of the 9th ACM Conference on Security &amp; Privacy in Wireless and Mobile Networks - WiSec &#39;16</a> </i> &nbsp;
In this paper we demonstrate how an attacker can infiltrate the home network via a doctored smart-phone app.  ...  The ability to infiltrate smart-homes via doctored smart-phone apps demonstrates that home routers are poor protection against Internet attacks and highlights the need for increased security for IoT devices  ...  WiSec' 16 , 16 July 18-22, 2016, Darmstadt, Germany c 2016 ACM.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2939918.2939925">doi:10.1145/2939918.2939925</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/wisec/SivaramanCEB16.html">dblp:conf/wisec/SivaramanCEB16</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gkvz7vpgvfeibcioy2q37k7vfy">fatcat:gkvz7vpgvfeibcioy2q37k7vfy</a> </span>
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Defeating MAC Address Randomization Through Timing Attacks

Célestin Matte, Mathieu Cunche, Franck Rousseau, Mathy Vanhoef
<span title="">2016</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qseddxfnjracne6jxnlhvkmmlu" style="color: black;">Proceedings of the 9th ACM Conference on Security &amp; Privacy in Wireless and Mobile Networks - WiSec &#39;16</a> </i> &nbsp;
We present an attack to defeat MAC address randomization through observation of the timings of the network scans with an offthe-shelf Wi-Fi interface.  ...  We propose several distance metrics based on timing and use them together with an incremental learning algorithm in order to group frames.  ...  WiSec' 16 , 16 July 18-22, 2016, Darmstadt, Germany ACM ISBN 978-1-4503-4270-4/16/07. . . $15.00 DOI: http://dx.doi.org/10.1145/2939918.2939930 Figure 2 : 2 Cumulative distribution of the distance D1  ... 
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More Semantics More Robust

Wei Chen, David Aspinall, Andrew D. Gordon, Charles Sutton, Igor Muttik
<span title="">2016</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qseddxfnjracne6jxnlhvkmmlu" style="color: black;">Proceedings of the 9th ACM Conference on Security &amp; Privacy in Wireless and Mobile Networks - WiSec &#39;16</a> </i> &nbsp;
We study the machine-learning-based mobile malware classifiers and reveal one reason: the input features used by these classifiers can't capture general behavioural patterns of malware instances.  ...  Automatic malware classifiers often perform badly on the detection of new malware, i.e., their robustness is poor.  ...  THE CLASSIFICATION PERFORMANCE OF CLUSTER-SPECIFIC CLASSIFIERS WiSec' 16 , 16 July 18-22, 2016, Darmstadt, Germany c 2016 ACM.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2939918.2939931">doi:10.1145/2939918.2939931</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/wisec/ChenAGSM16.html">dblp:conf/wisec/ChenAGSM16</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gpk6xcgkcnh3vdtzc7lund4gnq">fatcat:gpk6xcgkcnh3vdtzc7lund4gnq</a> </span>
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A Prover-Anonymous and Terrorist-Fraud Resistant Distance-Bounding Protocol

Xavier Bultel, Sébastien Gambs, David Gérault, Pascal Lafourcade, Cristina Onete, Jean-Marc Robert
<span title="">2016</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qseddxfnjracne6jxnlhvkmmlu" style="color: black;">Proceedings of the 9th ACM Conference on Security &amp; Privacy in Wireless and Mobile Networks - WiSec &#39;16</a> </i> &nbsp;
Acknowledgments We would like to thank the anonymous reviewers and our sheperd, Bart Preneel, who by their comments helped us to improve the quality of the paper and propose a deployment scenario for our  ...  WiSec '16, July 18-20, 2016 , Darmstadt, Germany. transport, logistics and contactless-payment systems.  ...  A recent concern in DB protocols is privacy. One of the first schemes to address this concept is the Swiss-Knife protocol [18] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2939918.2939919">doi:10.1145/2939918.2939919</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/wisec/BultelGGLOR16.html">dblp:conf/wisec/BultelGGLOR16</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/y56rxy4tfnaqndhbs7spvvxiiu">fatcat:y56rxy4tfnaqndhbs7spvvxiiu</a> </span>
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