Performance Analysis of Multi-Motion Sensor Behavior for Active Smartphone Authentication
- Submitting institution
-
University of Newcastle upon Tyne
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 273241-92335-1292
- Type
- D - Journal article
- DOI
-
10.1109/TIFS.2017.2737969
- Title of journal
- IEEE Transactions on Information Forensics and Security
- Article number
- -
- First page
- 48
- Volume
- 13
- Issue
- 1
- ISSN
- 1556-6013
- Open access status
- Deposit exception
- Month of publication
- August
- Year of publication
- 2017
- URL
-
https://doi.org/10.1109/TIFS.2017.2737969
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
-
E - Secure and Resilient Systems
- Citation count
- 64
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper undertakes a rigorous experimental analysis of smartphone sensor data as reflectors of user behavioral biometric signals and signatures representing a user's personal operational habits based on rhythm, strength, and angle preferences of finger movements. It is one of a series of papers investigating behavioral biometric phenomena. The false rejection rate and false acceptance rate were 5.03% and 3.98% respectively, representing an improvement on all previous work. Experimental protocols are laid out, evaluation methods are made clear, and procedures can be reproduced ... all of which are unusual in this field (and in empirical computer science in general).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -