With 95% Accuracy, New Acoustic Attack can Steal from Keystrokes

UK universities’ researchers have recently developed a deep learning model, designed to extract information from keyboard keystrokes collected using a microphone, with 95% accuracy.

The prediction accuracy decreased to 93% when Zoom was used to train the sound classification algorithm, still exceedingly good and a record for that medium.

Such an attack has a significantly adverse impact on the users’ data security since it is capable of exposing users’ passwords, conversations, messages, and other sensitive information to nefarious outsiders.

When compared to the other side attacks that need specific circumstances and are susceptible to data rate and distance restrictions, these acoustic attacks are easier to operate because of the popularity of devices that are now equipped with high-end microphones.

This makes sound-based side-channel attacks achievable and far more hazardous than previously thought, especially given the rapid advances in machine learning.

Listening to Keystrokes

The attack is initiated in order to acquire keystrokes on the victim’s keyboard, since the data is required for the prediction algorithm to work. This can be done via a nearby microphone or by accessing the microphone on the target’s phone, which may have been compromised by malware.

Additionally, keystrokes can also be recorded via Zoom call, in which, rogue

[…]
Content was cut in order to protect the source.Please visit the source for the rest of the article.

This article has been indexed from CySecurity News – Latest Information Security and Hacking Incidents