Smart Phone
We have created a new in-Air Signature dataset using Smart Phone that we called IASSP dataset. Forty participants voluntarily took part in each of the two databases’ construction. Each participant signs in the air five signatures and imitates five signatures of five other participants.
The participants were seated in a comfortable chair, with their dominant hand placed approximately 7 cm away from the camera of a smartphone, which was directly in front of them.
The data recorded on two files:
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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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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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