This paper presents the design and open source
		  implementation of CORMORANT , an Android authentication
		  framework able to increase usability and security of mobile
		  authentication. It uses transparent behavioral and
		  physiological biometrics like gait, face, voice, and
		  keystrokes dynamics to continuously evaluate the user’s
		  identity without explicit interaction. Using signals like
		  location, time of day, and nearby devices to assess the
		  risk of unauthorized access, the required level of
		  confidence in the user’s identity is dynamically
		  adjusted. Authentication results are shared securely,
		  end-to-end encrypted using the Signal messaging protocol,
		  with trusted devices to facilitate cross-device
		  authentication for co-located devices, detected using
		  Bluetooth low energy beacons. CORMORANT is able to reduce
		  the authentication overhead by up to 97% compared to
		  conventional knowledge-based authentication whilst
		  increasing security at the same time. We share our
		  perspective on some of the successes and shortcomings we
		  encountered implementing and evaluating CORMORANT to hope
		  to inform others working on similar projects.<br>
<br><pre>@inproceedings{Hintze_19_CORMORANTImplementingRisk,
  author = {Hintze, Daniel and F\"uller, Matthias and Scholz, Sebastian and Findling, Rainhard Dieter and Muaaz, Muhammad and Kapfer, Philipp and N\"ussler, Wilhelm and Mayrhofer, Ren\'e},
  booktitle = {Proc. {MoMM} 2019: 17th International Conference on
  		  Advances in Mobile Computing and Multimedia},
  title = {CORMORANT: On Implementing Risk-Aware Multi-Modal
  		  Biometric Cross-Device Authentication For Android},
  year = {2019},
  month = dec,
  number = { {In print}},
  publisher = {ACM}
}
</pre>