  In the digitized world, smartphones and their apps play an important role. To
name just a few examples, some apps offer possibilities for entertainment,
others for online banking, and others offer support for two-factor
authentication. Therefore, with smartphones also, sensitive information is
shared; thus, they are a desirable target for malware. The following technical
report gives an overview of how machine learning, especially neural networks,
can be employed to detect malicious Android apps based on their metadata.
Detection based on the metadata is necessary since not all of an app's
information is readable from another app due to the security layout of Android.
To do so, a comparable big dataset of metadata of apps has been collected for
learning and evaluation in this work. The first section, after the
introduction, presents the related work, followed by the description of the
sources of the dataset and the selection of the features used for machine
learning, in this case, only the app permissions. Afterward, a free available
dataset is used to find an efficient and effective neural network model for
learning and evaluation. Here, the fully connected network type consisting of
dense layers is chosen. Then this model is trained and evaluated on the new,
more extensive dataset to obtain a representative result. It turns out that
this model detects malware with an accuracy of 92.93% based on an app's
permissions.
