Spying on what is typed on a keyboard with Wi-Fi signals sounds scary but might not be as far from reality as suspected. Wi-Fi-enabled devices constantly measure the communication channel conditions represented with Channel State Information (CSI). Finger and hand movements alter the wireless signal propagation characteristic and cause changes in the CSI over time. Prior work proves it is possible to correlate the patterns in a CSI time series to the motion of keys pressed on a keyboard. This leaking information from Wi-Fi signal distortions can be exploited in a side-channel keylogging attack.

Typing is a prevalent activity when it comes to working with computers on a regular basis. Considering that what we type reveals not only private messages like emails or notes but also highly sensitive data such as passwords or banking information, this leaves a frightening prospect.

In this thesis, we practically explore the potential threat of side-channel keylogging attacks with CSI by implementing and comparing the conventional method found in related work to deep learning-based approaches to infer keystrokes. Motivated by the fact that the use of deep learning models promises less effort in pre-processing and feature extraction, we apply deep learning approaches for the first time for CSI-based keylogging and extend the knowledge about the applications of Deep Neural Networks (DNNs).

We create a dataset worth more than 24 hours of recording time with a controlled experimental setup to empirically evaluate the performance of the implemented keyloggers. Our results indicate the difficulties and limitations our keylogging models face, which renders keylogging attacks with Wi-Fi signals rather cumbersome for real-world attackers.