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

Date

2020

Advisors

Journal Title

Journal ISSN

ISSN

1868-5137

Volume Title

Publisher

Springer

Type

Article

Peer reviewed

Yes

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.

Description

The file attached to this record is the author's final peer reviewed version

Keywords

Distributed Computing, Artificial Intelligence and Image Processing

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,

Rights

Research Institute