| 1. | MO Spata; VM Russo; A Ortis; S Battiato: A New Pipeline for Snooping Keystroke Based on Deep Learning Algorithm. In: IEEE Access, 13 , pp. 24498 - 24514, 2025. (Type: Journal Article | Abstract | Links | BibTeX) @article{Spata2025, title = {A New Pipeline for Snooping Keystroke Based on Deep Learning Algorithm}, author = {MO Spata and VM Russo and A Ortis and S Battiato}, editor = {IEEE}, url = {https://ieeexplore.ieee.org/abstract/document/10858134}, doi = {10.1109/ACCESS.2025.3536877}, year = {2025}, date = {2025-01-29}, journal = {IEEE Access}, volume = {13}, pages = {24498 - 24514}, abstract = {This research focuses on the vulnerabilities of keystroke by logging on a physical computer keyboard, known as Snooping Keystroke. This category of attacks occurs recording an audio track with a smartphone while typing on the keyboard, and processing the audio to detect individual pressed keys. To address this issue, mathematical wavelet transforms have been tested, while key recognition has been implemented using the inference test of a deep learning model based on a Temporal Convolutional Network (TCN). The novelty of the proposed pipeline lies in its dynamic audio analysis and keystroke recognition, which splits the wave based on audio signal peaks generated by key presses. This approach enables an attack in real-world conditions without knowing the exact number of keystrokes typed by the user. Experimental results show a peak accuracy of 98.3%.}, keywords = {}, pubstate = {published}, tppubtype = {article} } This research focuses on the vulnerabilities of keystroke by logging on a physical computer keyboard, known as Snooping Keystroke. This category of attacks occurs recording an audio track with a smartphone while typing on the keyboard, and processing the audio to detect individual pressed keys. To address this issue, mathematical wavelet transforms have been tested, while key recognition has been implemented using the inference test of a deep learning model based on a Temporal Convolutional Network (TCN). The novelty of the proposed pipeline lies in its dynamic audio analysis and keystroke recognition, which splits the wave based on audio signal peaks generated by key presses. This approach enables an attack in real-world conditions without knowing the exact number of keystrokes typed by the user. Experimental results show a peak accuracy of 98.3%. |
2025 |
MO Spata; VM Russo; A Ortis; S Battiato A New Pipeline for Snooping Keystroke Based on Deep Learning Algorithm Journal Article IEEE Access, 13 , pp. 24498 - 24514, 2025. Abstract | Links | BibTeX | Tags: Acoustic side channel attack, Deep learning, laptop keystroke attacks, snooping keystroke attacks, user security and privacy, zoom-based acoustic attacks @article{Spata2025, title = {A New Pipeline for Snooping Keystroke Based on Deep Learning Algorithm}, author = {MO Spata and VM Russo and A Ortis and S Battiato}, editor = {IEEE}, url = {https://ieeexplore.ieee.org/abstract/document/10858134}, doi = {10.1109/ACCESS.2025.3536877}, year = {2025}, date = {2025-01-29}, journal = {IEEE Access}, volume = {13}, pages = {24498 - 24514}, abstract = {This research focuses on the vulnerabilities of keystroke by logging on a physical computer keyboard, known as Snooping Keystroke. This category of attacks occurs recording an audio track with a smartphone while typing on the keyboard, and processing the audio to detect individual pressed keys. To address this issue, mathematical wavelet transforms have been tested, while key recognition has been implemented using the inference test of a deep learning model based on a Temporal Convolutional Network (TCN). The novelty of the proposed pipeline lies in its dynamic audio analysis and keystroke recognition, which splits the wave based on audio signal peaks generated by key presses. This approach enables an attack in real-world conditions without knowing the exact number of keystrokes typed by the user. Experimental results show a peak accuracy of 98.3%.}, keywords = {Acoustic side channel attack, Deep learning, laptop keystroke attacks, snooping keystroke attacks, user security and privacy, zoom-based acoustic attacks}, pubstate = {published}, tppubtype = {article} } This research focuses on the vulnerabilities of keystroke by logging on a physical computer keyboard, known as Snooping Keystroke. This category of attacks occurs recording an audio track with a smartphone while typing on the keyboard, and processing the audio to detect individual pressed keys. To address this issue, mathematical wavelet transforms have been tested, while key recognition has been implemented using the inference test of a deep learning model based on a Temporal Convolutional Network (TCN). The novelty of the proposed pipeline lies in its dynamic audio analysis and keystroke recognition, which splits the wave based on audio signal peaks generated by key presses. This approach enables an attack in real-world conditions without knowing the exact number of keystrokes typed by the user. Experimental results show a peak accuracy of 98.3%. |