Smart Phone

Wi-Fi signal strength measurements from smartphone for various hand gestures


The dataset is an extensive collection of labeled high-frequency Wi-Fi Radio Signal Strength (RSS) measurements corresponding to multiple hand gestures made near a smartphone under different spatial and data traffic scenarios. We open source the software code and an Android app (Winiff) to create this dataset, which is available at Github (https://github.com/mohaseeb/wisture). The dataset is created using an artificial traffic induction (between the phone and the access point) approach to enable useful and meaningful RSS value

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Dataset Details

Citation Author(s):
Submitted by:
Ramviyas Parasuraman
Last updated:
Mon, 01/08/2018 - 21:53
DOI:
10.21227/H2C362
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[1] , "Wi-Fi signal strength measurements from smartphone for various hand gestures", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2C362. Accessed: Jan. 21, 2018.
@data{h2c362-18,
doi = {10.21227/H2C362},
url = {http://dx.doi.org/10.21227/H2C362},
author = { },
publisher = {IEEE Dataport},
title = {Wi-Fi signal strength measurements from smartphone for various hand gestures},
year = {2018} }
TY - DATA
T1 - Wi-Fi signal strength measurements from smartphone for various hand gestures
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2C362
ER -
. (2018). Wi-Fi signal strength measurements from smartphone for various hand gestures. IEEE Dataport. http://dx.doi.org/10.21227/H2C362
, 2018. Wi-Fi signal strength measurements from smartphone for various hand gestures. Available at: http://dx.doi.org/10.21227/H2C362.
. (2018). "Wi-Fi signal strength measurements from smartphone for various hand gestures." Web.
1. . Wi-Fi signal strength measurements from smartphone for various hand gestures [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2C362
. "Wi-Fi signal strength measurements from smartphone for various hand gestures." doi: 10.21227/H2C362

Dataset malware/beningn permissions Android


This dataset is a result of my research production into machine learning in android security. The data was obtained by a process that consisted to map a binary vector of permissions used for each application analyzed {1=used, 0=no used}. Moreover, the samples of malware/benign were devided by "Type"; 1 malware and 0 non-malware.

When I did my research, the datasets of malware and benign Android applications were not available, then I give to the community a part of my research results for the future works.

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Dataset Details

Citation Author(s):
Submitted by:
Christian Urcuqui
Last updated:
Thu, 11/17/2016 - 10:43
DOI:
10.21227/H26P4M
Data Format:
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[1] , "Dataset malware/beningn permissions Android", IEEE Dataport, 2016. [Online]. Available: http://dx.doi.org/10.21227/H26P4M. Accessed: Jan. 21, 2018.
@data{h26p4m-16,
doi = {10.21227/H26P4M},
url = {http://dx.doi.org/10.21227/H26P4M},
author = { },
publisher = {IEEE Dataport},
title = {Dataset malware/beningn permissions Android},
year = {2016} }
TY - DATA
T1 - Dataset malware/beningn permissions Android
AU -
PY - 2016
PB - IEEE Dataport
UR - 10.21227/H26P4M
ER -
. (2016). Dataset malware/beningn permissions Android. IEEE Dataport. http://dx.doi.org/10.21227/H26P4M
, 2016. Dataset malware/beningn permissions Android. Available at: http://dx.doi.org/10.21227/H26P4M.
. (2016). "Dataset malware/beningn permissions Android." Web.
1. . Dataset malware/beningn permissions Android [Internet]. IEEE Dataport; 2016. Available from : http://dx.doi.org/10.21227/H26P4M
. "Dataset malware/beningn permissions Android." doi: 10.21227/H26P4M