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Javed, AR, Beg, MO, Asim, M, Baker, T and Al-Bayatti, AH (2020) AplhaLogger: Detecting Motion-based Side-Channel Attack Using Smartphone Keystrokes. Journal of Ambient Intelligence and Humanized Computing. ISSN 1868-5137
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Abstract
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 |
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| Additional Information: | This is a post-peer-review, pre-copyedit version of an article published in Journal of Ambient Intelligence and Humanized Computing. The final authenticated version is available online at: http://dx.doi.org/10.1007/s12652-020-01770-0 |
| 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 and Mathematics |
| Publisher: | Springer (part of Springer Nature) |
| Date of acceptance: | 6 February 2020 |
| Date of first compliant Open Access: | 13 January 2022 |
| Date Deposited: | 06 Feb 2020 09:35 |
| Last Modified: | 13 Jan 2022 11:45 |
| DOI or ID number: | 10.1007/s12652-020-01770-0 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/12198 |
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