Dataset Open Access

Index of supplementary files from "Perils of Zero-Interaction Security in the Internet of Things"

Fomichev, Mikhail; Maass, Max; Almon, Lars; Molina, Alejandro; Hollick, Matthias


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    <subfield code="a">Index of supplementary files from "Perils of Zero-Interaction Security in the Internet of Things"</subfield>
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    <subfield code="a">&lt;p&gt;This record serves an an index to the other dataset releases that are part of the paper &amp;quot;Perils of Zero Interaction Security in the Internet of Things&amp;quot; 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.&lt;/p&gt;

&lt;p&gt;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:&lt;/p&gt;

&lt;ol&gt;
	&lt;li&gt;&lt;strong&gt;Raw data&lt;/strong&gt;&lt;br&gt;
	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:
	&lt;ol&gt;
		&lt;li&gt;&lt;a href="http://dx.doi.org/10.5281/zenodo.2537699"&gt;Car Scenario&lt;/a&gt;&lt;/li&gt;
		&lt;li&gt;&lt;a href="http://dx.doi.org/10.5281/zenodo.2537701"&gt;Office Scenario&lt;/a&gt;&lt;/li&gt;
		&lt;li&gt;&lt;a href="http://dx.doi.org/10.5281/zenodo.2537703"&gt;Mobile Scenario&lt;/a&gt; + &lt;a href="http://dx.doi.org/10.5281/zenodo.2537984"&gt;audio data in separate deposit&lt;/a&gt; (with access control)&lt;/li&gt;
	&lt;/ol&gt;
	&lt;/li&gt;
	&lt;li&gt;&lt;strong&gt;Processed Data&lt;/strong&gt;&lt;br&gt;
	The processed data is generated from the raw data using the processing code (which can be found in &lt;a href="https://dx.doi.org/10.5281/zenodo.2543721"&gt;the code repository&lt;/a&gt;). 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:
	&lt;ol&gt;
		&lt;li&gt;&lt;a href="http://dx.doi.org/10.5281/zenodo.2537705"&gt;Car Scenario&lt;/a&gt;&lt;/li&gt;
		&lt;li&gt;&lt;a href="http://dx.doi.org/10.5281/zenodo.2537707"&gt;Office Scenario&lt;/a&gt;&lt;/li&gt;
		&lt;li&gt;&lt;a href="http://dx.doi.org/10.5281/zenodo.2537709"&gt;Mobile Scenario&lt;/a&gt;&lt;/li&gt;
	&lt;/ol&gt;
	&lt;/li&gt;
	&lt;li&gt;&lt;strong&gt;Result Data&lt;/strong&gt;&lt;br&gt;
	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 &lt;a href="http://dx.doi.org/10.5281/zenodo.2543721"&gt;source code repository&lt;/a&gt;. Here, five datasets exist, one for each investigated paper:
	&lt;ol&gt;
		&lt;li&gt;&lt;a href="http://dx.doi.org/10.5281/zenodo.2537711"&gt;Karapanos et al.&lt;/a&gt;&lt;/li&gt;
		&lt;li&gt;&lt;a href="http://dx.doi.org/10.5281/zenodo.2537713"&gt;Sch&amp;uuml;rmann and Sigg&lt;/a&gt;&lt;/li&gt;
		&lt;li&gt;&lt;a href="http://dx.doi.org/10.5281/zenodo.2537715"&gt;Miettinen et al.&lt;/a&gt;&lt;/li&gt;
		&lt;li&gt;&lt;a href="http://dx.doi.org/10.5281/zenodo.2537717"&gt;Truong et al.&lt;/a&gt;&lt;/li&gt;
		&lt;li&gt;&lt;a href="http://dx.doi.org/10.5281/zenodo.2537719"&gt;Shrestha et al.&lt;/a&gt;&lt;/li&gt;
	&lt;/ol&gt;
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