AlphaLogger: detecting motion-based side-channel attack using smartphone keystrokes

Abdul Rehman Javed, Mirza Omer Beg, Muhammad Asim, Thar Baker, Ali Hilal Al-Bayatti

Research output: Contribution to journalArticlepeer-review


Due to the advancement in technologies and excessive usability of smartphones in various domains (e.g., mobile banking), smartphones became more prone to malicious attacks.Typing on the soft keyboard of a smartphone produces different vibrations, which can be abused to recognize the keys being pressed, hence, facilitating side-channel attacks. In this work, we develop and evaluate AlphaLogger- an Android-based application that infers the alphabet keys being typed on a soft keyboard. AlphaLogger runs in the background and collects data at a frequency of 10Hz/sec from the smartphone hardware sensors (accelerometer, gyroscope and magnetometer) to accurately infer the keystrokes being typed on the soft keyboard of all other applications running in the foreground. We show a performance analysis of the different combinations of sensors. A thorough evaluation demonstrates that keystrokes can be inferred with an accuracy of 90.2% using accelerometer, gyroscope, and magnetometer.

Original languageEnglish
JournalJournal of Ambient Intelligence and Humanized Computing
Publication statusPublished - 15 Feb 2020

Bibliographical note

Publisher Copyright:
© 2020, Springer-Verlag GmbH Germany, part of Springer Nature.


  • Keystroke inference
  • Machine learning
  • Motion sensor
  • Side-channel attacks
  • Smartphone security


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