A group of academics has devised a “deep learning-based acoustic side-channel attack” that can be used to classify laptop keystrokes that are recorded using a nearby phone with 95% accuracy.
“When trained on keystrokes recorded using the video conferencing software Zoom, an accuracy of 93% was achieved, a new best for the medium,” researchers Joshua Harrison, Ehsan Toreini, and Maryam Mehrnezhad said in a new study published last week.
Side-channel attacks refer to a class of security exploits that aim to glean insights from a system by monitoring and measuring its physical effects during the processing of sensitive data. Some of the common observable effects include runtime behavior, power consumption, electromagnetic radiation, acoustics, and cache accesses.
