A New Pipeline for Snooping Keystroke Based on Deep Learning Algorithm
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 a...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10858134/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823859598587592704 |
|---|---|
| author | Massimo Orazio Spata Valerio Maria Russo Alessandro Ortis Sebastiano Battiato |
| author_facet | Massimo Orazio Spata Valerio Maria Russo Alessandro Ortis Sebastiano Battiato |
| author_sort | Massimo Orazio Spata |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-2c26918ae91545fd9e739f4fbac9f569 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-2c26918ae91545fd9e739f4fbac9f5692025-02-11T00:01:08ZengIEEEIEEE Access2169-35362025-01-0113244982451410.1109/ACCESS.2025.353687710858134A New Pipeline for Snooping Keystroke Based on Deep Learning AlgorithmMassimo Orazio Spata0https://orcid.org/0000-0001-6704-7485Valerio Maria Russo1https://orcid.org/0009-0009-6949-2617Alessandro Ortis2https://orcid.org/0000-0003-3461-4679Sebastiano Battiato3https://orcid.org/0000-0001-6127-2470Dipartimento di Matematica e Informatica, University of Catania, Catania, ItalyDipartimento di Matematica e Informatica, University of Catania, Catania, ItalyDipartimento di Matematica e Informatica, University of Catania, Catania, ItalyDipartimento di Matematica e Informatica, University of Catania, Catania, ItalyThis 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%.https://ieeexplore.ieee.org/document/10858134/Acoustic side channel attacksnooping keystroke attacksdeep learninguser security and privacylaptop keystroke attackszoom-based acoustic attacks |
| spellingShingle | Massimo Orazio Spata Valerio Maria Russo Alessandro Ortis Sebastiano Battiato A New Pipeline for Snooping Keystroke Based on Deep Learning Algorithm IEEE Access Acoustic side channel attack snooping keystroke attacks deep learning user security and privacy laptop keystroke attacks zoom-based acoustic attacks |
| title | A New Pipeline for Snooping Keystroke Based on Deep Learning Algorithm |
| title_full | A New Pipeline for Snooping Keystroke Based on Deep Learning Algorithm |
| title_fullStr | A New Pipeline for Snooping Keystroke Based on Deep Learning Algorithm |
| title_full_unstemmed | A New Pipeline for Snooping Keystroke Based on Deep Learning Algorithm |
| title_short | A New Pipeline for Snooping Keystroke Based on Deep Learning Algorithm |
| title_sort | new pipeline for snooping keystroke based on deep learning algorithm |
| topic | Acoustic side channel attack snooping keystroke attacks deep learning user security and privacy laptop keystroke attacks zoom-based acoustic attacks |
| url | https://ieeexplore.ieee.org/document/10858134/ |
| work_keys_str_mv | AT massimooraziospata anewpipelineforsnoopingkeystrokebasedondeeplearningalgorithm AT valeriomariarusso anewpipelineforsnoopingkeystrokebasedondeeplearningalgorithm AT alessandroortis anewpipelineforsnoopingkeystrokebasedondeeplearningalgorithm AT sebastianobattiato anewpipelineforsnoopingkeystrokebasedondeeplearningalgorithm AT massimooraziospata newpipelineforsnoopingkeystrokebasedondeeplearningalgorithm AT valeriomariarusso newpipelineforsnoopingkeystrokebasedondeeplearningalgorithm AT alessandroortis newpipelineforsnoopingkeystrokebasedondeeplearningalgorithm AT sebastianobattiato newpipelineforsnoopingkeystrokebasedondeeplearningalgorithm |