Abstract
With the rapid growth of smartphone shipments, it has become a necessity to safeguard the mobile devices from unauthorized access (e.g., in case of stolen or lost). To complement traditional textual passwords, graphical password is believed to be a promising alternative, which requires users to create their credentials based on image(s). However, many complicated graphical password schemes may increase the memory load of a user. In practice, a usable graphical password scheme is often designed based on users’ own knowledge. In this work, we introduce PassFile, a graphical password authentication scheme based on file browsing records on mobile devices. It requires a user to select the most frequently used applications from the mobile devices as authentication token. In the user study, our results indicate that our proposed PassFile can provide a high login success rate (over 96%). Copyright © 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Original language | English |
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Title of host publication | Machine learning for cyber security: Proceedings of 5th International Conference, ML4CS 2023 |
Editors | Dan Dongseong KIM, Chao CHEN |
Place of Publication | Singapore |
Publisher | Springer |
Pages | 28-43 |
ISBN (Print) | 9789819724574 |
DOIs | |
Publication status | Published - 2024 |
Citation
Fu, H. C., Li, W., & Wang, Y. (2024). PassFile: Graphical password authentication based on file browsing records. In D. D. Kim, & C. Chen (Eds.), Machine learning for cyber security: Proceedings of 5th International Conference, ML4CS 2023 (pp. 28-43). Springer. https://doi.org/10.1007/978-981-97-2458-1_3Keywords
- User authentication
- File browsing record
- Unlock mechanism
- Mobile device
- PassFile
- Graphical password