Using Machine Learning to Detect Keystrokes
Researchers have successfully trained a machine learning (ML) model to detect keystrokes by sound with an impressive accuracy of 95%. In their paper titled “A Practical Deep Learning-Based Acoustic Side Channel Attack on Keyboards,” the researchers discuss the rising threat of acoustic side channel attacks on keyboards due to the advancements in deep learning, the prevalence of microphones, and the increasing use of online services through personal devices. The study presents a practical implementation of a state-of-the-art deep learning model that can classify laptop keystrokes using a smartphone’s integrated microphone.
The ML model achieved the highest accuracy of 95% when trained on keystrokes recorded by a nearby phone, without the use of a language model. Additionally, when trained on keystrokes recorded using the popular video-conferencing software Zoom, the model achieved an accuracy of 93%, setting a new benchmark for this medium. The results demonstrate the practicality of these side channel attacks using off-the-shelf equipment and algorithms.
To protect users against these types of attacks, the researchers discuss a series of mitigation methods. By understanding the vulnerabilities and implementing appropriate defenses, individuals and organizations can safeguard their sensitive information from potential acoustic side channel attacks.
This breakthrough in using machine learning to detect keystrokes has garnered attention from the media. An article published on Bleeping Computer provides further insights into the research and its implications.
Key Points:
– Researchers have trained an ML model to detect keystrokes by sound with 95% accuracy.
– The study presents a practical implementation of a deep learning model to classify laptop keystrokes using a smartphone’s microphone.
– The model achieved an accuracy of 95% when trained on keystrokes recorded by a nearby phone and 93% when trained on keystrokes recorded using Zoom.
– The results highlight the practicality of acoustic side channel attacks using readily available equipment and algorithms.
– Mitigation methods are discussed to protect against these types of attacks.
