AlphaLogger: Detecting Motion-based Side-Channel Attack Using Smartphone Keystrokes
| dc.cclicence | CC-BY-NC | en |
| dc.contributor.author | Javed, A.R. | |
| dc.contributor.author | Baker, T. | |
| dc.contributor.author | Asim, M. | |
| dc.contributor.author | Beg, M.O. | |
| dc.contributor.author | Al-Bayatti, Ali Hilal | |
| dc.date.acceptance | 2020-02-05 | |
| dc.date.accessioned | 2020-02-13T13:11:03Z | |
| dc.date.available | 2020-02-13T13:11:03Z | |
| dc.date.issued | 2020 | |
| dc.description | The file attached to this record is the author's final peer reviewed version | en |
| dc.description.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. | en |
| dc.funder | No external funder | en |
| dc.identifier.citation | Javed, A.R., Beg, M.O., Asim, M., Baker, T. and Al-Bayatti, A.H. (2020) AlphaLogger: Detecting Motion-based Side-Channel Attack Using Smartphone Keystrokes. Journal of Ambient Intelligence and Humanized Computing, | en |
| dc.identifier.doi | https://doi.org/10.1007/s12652-020-01770-0 | |
| dc.identifier.issn | 1868-5137 | |
| dc.identifier.uri | https://dora.dmu.ac.uk/handle/2086/19171 | |
| dc.language.iso | en | en |
| dc.peerreviewed | Yes | en |
| dc.publisher | Springer | en |
| dc.researchinstitute | Cyber Technology Institute (CTI) | en |
| dc.subject | Distributed Computing | en |
| dc.subject | Artificial Intelligence and Image Processing | en |
| dc.title | AlphaLogger: Detecting Motion-based Side-Channel Attack Using Smartphone Keystrokes | en |
| dc.type | Article | en |