  Wearable technologies are today on the rise, becoming more common and broadly
available to mainstream users. In fact, wristband and armband devices such as
smartwatches and fitness trackers already took an important place in the
consumer electronics market and are becoming ubiquitous. By their very nature
of being wearable, these devices, however, provide a new pervasive attack
surface threatening users privacy, among others.
  In the meantime, advances in machine learning are providing unprecedented
possibilities to process complex data efficiently. Allowing patterns to emerge
from high dimensional unavoidably noisy data.
  The goal of this work is to raise awareness about the potential risks related
to motion sensors built-in wearable devices and to demonstrate abuse
opportunities leveraged by advanced neural network architectures.
  The LSTM-based implementation presented in this research can perform
touchlogging and keylogging on 12-keys keypads with above-average accuracy even
when confronted with raw unprocessed data. Thus demonstrating that deep neural
networks are capable of making keystroke inference attacks based on motion
sensors easier to achieve by removing the need for non-trivial pre-processing
pipelines and carefully engineered feature extraction strategies. Our results
suggest that the complete technological ecosystem of a user can be compromised
when a wearable wristband device is worn.
