A team of researchers from British universities has trained a deep learning model that can steal data from keyboard keystrokes recorded using a microphone with an accuracy of 95%. When Zoom was used for training the sound classification algorithm, the prediction accuracy dropped to 93%, which is still dangerously high, and a record for that medium. Such an attack severely affects the target’s data security, as it could leak people’s passwords, discussions, messages, or other sensitive information to malicious third parties. Moreover, contrary to other side-channel attacks that require special conditions and are subject to data rate and distance limitations, acoustic attacks have become much simpler due to the abundance of microphone-bearing devices that can achieve high-quality audio captures. This, combined with the rapid advancements in machine learning, makes sound-based side-channel attacks feasible and a lot more dangerous than previously anticipated. The first step of the attack is to record keystrokes on the target’s keyboard, as that data is required for training the prediction algorithm. This can be achieved via a nearby microphone or the target’s phone that might have been infected by malware that has access to its microphone.
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