PushPIN: A Pressure-Based Behavioral Biometric Authentication System for Smartwatches

Abstract

Smartwatches support diverse applications but suffer from security issues due to their limited resources; their small size poorly supports the rich, accurate input required for screen lock authentication. Additionally, traditional approaches to unlocking smart devices, such as Personal identification number, are highly susceptible to attacks such as guessing and video observation. Therefore, we propose PushPIN, a novel scheme that combines knowledge-based and behavioral biometric approaches to increase security. Input symbols are composed of the selection of one of four different targets with one of five different pressure levels, for a total of 20 possibilities. We complement this passcode by capturing behavioral biometric features from screen touches and wrist motion during input. We present two studies to assess the performance of PushPIN. The first assesses both usability and security against a random guessing attack. It shows acceptable usability—recall times of approximately 8 s and no errors—and strong security: equal error rates of 0.51%. The second study examines the resistance of PushPIN against a video observation attack, ultimately revealing that 36.67% of PushPINs could be cracked, performance that represents a substantial improvement over prior work on pressure-based authentication input. We conclude that pressure-based input can increase the security, while maintaining reasonable usability, of smartwatch lock systems.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.

Notes

1 http://freebrainagegames.com/recall.html

Additional information

Funding

This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT [2020R1F1A1070699].

Notes on contributors

Youngeun Song

Youngeun Song is a Ph.D. candidate at the Ulsan National Institute of Science and Technology, Republic of Korea. Her research is in human-computer interaction and specifically, the trade-offs between usability and security on wearables.

Ian Oakley

Ian Oakley received his Ph.D. in Computer Science from the University of Glasgow, UK and is now a full professor at the Department of Design at Ulsan National Institute of Science and Technology. His research focuses on the design, development and evaluation of multi-modal interfaces and social technologies.