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AplhaLogger: Detecting Motion-based Side-Channel Attack Using Smartphone Keystrokes

Javed, AR, Beg, MO, Asim, M, Baker, T and Al-Bayatti, AH AplhaLogger: Detecting Motion-based Side-Channel Attack Using Smartphone Keystrokes. Journal of Ambient Intelligence and Humanized Computing. ISSN 1868-5137 (Accepted)

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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.

Item Type: Article
Uncontrolled Keywords: 0805 Distributed Computing, 0801 Artificial Intelligence and Image Processing
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Computer Science & Mathematics
Publisher: Springer (part of Springer Nature)
Date Deposited: 06 Feb 2020 09:35
Last Modified: 06 Feb 2020 09:45
URI: https://researchonline.ljmu.ac.uk/id/eprint/12198

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