A Practical Deep Learning-Based Acoustic Side Channel Attack on
  Keyboards
Joshua Harrison Ehsan Toreini Maryam Mehrnezhad
With recent developments in deep learning, the ubiquity of micro-phones and
the rise in online services via personal devices, acoustic side channel attacks
present a greater threat to keyboards than ever. This paper presents a
practical implementation of a state-of-the-art deep learning model in order to
classify laptop keystrokes, using a smartphone integrated microphone. When
trained on keystrokes recorded by a nearby phone, the classifier achieved an
accuracy of 95%, the highest accuracy seen without the use of a language model.
When trained on keystrokes recorded using the video-conferencing software Zoom,
an accuracy of 93% was achieved, a new best for the medium. Our results prove
the practicality of these side channel attacks via off-the-shelf equipment and
algorithms. We discuss a series of mitigation methods to protect users against
these series of attacks.