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Updated: Jun 10, 2025

An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones

An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones

Published on: October 5, 2020

Multi-datasets for different keyboard key sound recognition.

Karwan M Hama Rawf¹, Ayub O Abdulrahman¹, Hana O Kamel¹

¹Department of Computer Science, College of Science, University of Halabja, Halabja, Kurdistan Region, F.R. Iraq.

|October 11, 2024

Summary

This summary is machine-generated.

The new Multi-Keyboard Acoustic (MKA) Datasets offer extensive keyboard sound recordings for cybersecurity research. These datasets aid in developing better defenses against acoustic threats and keylogging detection.

Keywords:

Acoustic side-channel attacks Keyboard security Keyboard sound classification Multi-datasets Signal Processing Social media platforms Sound Recognition

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Area of Science:

Cybersecurity and Human-Computer Interaction
Acoustic Signal Processing

Background:

Keyboard acoustic recognition is vital for cybersecurity and HCI.
System performance is affected by platform, typing style, and noise.

Purpose of the Study:

Introduce the Multi-Keyboard Acoustic (MKA) Datasets.
Provide a comprehensive resource for acoustic keyboard analysis.
Facilitate research in keylogging detection and acoustic emanation attacks.

Main Methods:

Collected recordings from six platforms (HP, Lenovo, MSI, Mac, Messenger, Zoom).
Structured data includes raw recordings, segmented sounds, and derived matrices.
Utilized Praat tool for precise data segmentation and pre-processing.

Main Results:

MKA Datasets are among the largest and most detailed in the domain.
Data captures typing intricacies using both hands and all ten fingers.
Ensures high-quality, dependable data for research.

Conclusions:

MKA Datasets significantly advance keyboard sound recognition research.
Contribute to developing robust recognition algorithms.
Enhance defenses against acoustic-based security threats.