2537721
doi
10.5281/zenodo.2537721
oai:zenodo.org:2537721
user-zis
Maass, Max
Secure Mobile Networking Lab, TU Darmstadt
Almon, Lars
Secure Mobile Networking Lab, TU Darmstadt
Molina, Alejandro
Machine Learning Group, TU Darmstadt
Hollick, Matthias
Secure Mobile Networking Lab, TU Darmstadt
Index of supplementary files from "Perils of Zero-Interaction Security in the Internet of Things"
Fomichev, Mikhail
Secure Mobile Networking Lab, TU Darmstadt
doi:10.5281/zenodo.2537719
doi:10.5281/zenodo.2537717
doi:10.5281/zenodo.2537715
doi:10.5281/zenodo.2537713
doi:10.5281/zenodo.2537711
doi:10.5281/zenodo.2537707
doi:10.5281/zenodo.2537705
doi:10.5281/zenodo.2537709
doi:10.5281/zenodo.2537699
doi:10.5281/zenodo.2537701
doi:10.5281/zenodo.2537703
doi:10.5281/zenodo.2537984
doi:10.5281/zenodo.2543721
arxiv:arXiv:1901.07255v1
info:eu-repo/semantics/openAccess
Open Data Commons Attribution License v1.0
https://opendatacommons.org/licenses/by/1.0/
<p>This record serves an an index to the other dataset releases that are part of the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1.</p>
<p>We have chosen to split the dataset into several parts to meet Zenodo size requirements and make it easier to find specific pieces of data. In total, the following datasets exist:</p>
<ol>
<li><strong>Raw data</strong><br>
These datasets contain raw data, as collected directly from the devices doing the recording. It includes readings from several different sensors, as well as observed WiFi and BLE signals with their signal strength, and in one case, audio recordings. This raw data can be used to repeat our own experiments, or to apply different schemes to it to have a baseline for comparisons. Four datasets exist, mapped to the three scenarios discussed in the paper:
<ol>
<li><a href="http://dx.doi.org/10.5281/zenodo.2537699">Car Scenario</a></li>
<li><a href="http://dx.doi.org/10.5281/zenodo.2537701">Office Scenario</a></li>
<li><a href="http://dx.doi.org/10.5281/zenodo.2537703">Mobile Scenario</a> + <a href="http://dx.doi.org/10.5281/zenodo.2537984">audio data in separate deposit</a> (with access control)</li>
</ol>
</li>
<li><strong>Processed Data</strong><br>
The processed data is generated from the raw data using the processing code (which can be found in <a href="https://dx.doi.org/10.5281/zenodo.2543721">the code repository</a>). The resulting data contains computed features from the five papers under investigation plus derived machine learning datasets, and can be used to see in detail how the schemes behave in specific situations. These datasets tend to be fairly large. Three datasets exist:
<ol>
<li><a href="http://dx.doi.org/10.5281/zenodo.2537705">Car Scenario</a></li>
<li><a href="http://dx.doi.org/10.5281/zenodo.2537707">Office Scenario</a></li>
<li><a href="http://dx.doi.org/10.5281/zenodo.2537709">Mobile Scenario</a></li>
</ol>
</li>
<li><strong>Result Data</strong><br>
Finally, the result datasets contain the results of the evaluation (i.e., the computed error rates and generated plots, plus associated caches). The code used to derive these results can once again be found in the <a href="http://dx.doi.org/10.5281/zenodo.2543721">source code repository</a>. Here, five datasets exist, one for each investigated paper:
<ol>
<li><a href="http://dx.doi.org/10.5281/zenodo.2537711">Karapanos et al.</a></li>
<li><a href="http://dx.doi.org/10.5281/zenodo.2537713">Schürmann and Sigg</a></li>
<li><a href="http://dx.doi.org/10.5281/zenodo.2537715">Miettinen et al.</a></li>
<li><a href="http://dx.doi.org/10.5281/zenodo.2537717">Truong et al.</a></li>
<li><a href="http://dx.doi.org/10.5281/zenodo.2537719">Shrestha et al.</a></li>
</ol>
</li>
</ol>
Zenodo
2019-01-11
info:eu-repo/semantics/other
2537720
user-zis
1579893895.390995
2699
md5:fd18f12482858a2d4528e254af4798af
https://zenodo.org/records/2537721/files/README.md
public
10.5281/zenodo.2537719
Has part
doi
10.5281/zenodo.2537717
Has part
doi
10.5281/zenodo.2537715
Has part
doi
10.5281/zenodo.2537713
Has part
doi
10.5281/zenodo.2537711
Has part
doi
10.5281/zenodo.2537707
Has part
doi
10.5281/zenodo.2537705
Has part
doi
10.5281/zenodo.2537709
Has part
doi
10.5281/zenodo.2537699
Has part
doi
10.5281/zenodo.2537701
Has part
doi
10.5281/zenodo.2537703
Has part
doi
10.5281/zenodo.2537984
Has part
doi
10.5281/zenodo.2543721
Has part
doi
arXiv:1901.07255v1
Is documented by
arxiv
10.5281/zenodo.2537720
isVersionOf
doi