Keystroke Mobile Authentication: Performance of Long-Term Approaches and Fusion with Behavioral Profiling

被引:5
|
作者
Acien, Alejandro [1 ]
Morales, Aythami [1 ]
Vera-Rodriguez, Ruben [1 ]
Fierrez, Julian [1 ]
机构
[1] Univ Autonoma Madrid, Sch Engn, BiDA Lab, C Francisco Tomas y Valiente 11, Madrid 28049, Spain
来源
PATTERN RECOGNITION AND IMAGE ANALYSIS, IBPRIA 2019, PT II | 2019年 / 11868卷
关键词
Mobile authentication; Biometric recognition; Behavioral pattern; Behavioral-based profiling; Keystroke dynamics;
D O I
10.1007/978-3-030-31321-0_2
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper we evaluate the performance of mobile keystroke authentication according to: (1) data availability to model the user; and (2) combination with behavioral-based profiling techniques. We have developed an ensemble of three behavioral based-profile authentication techniques (WiFi, GPS Location, and App usage) and a Keystroke state-of-the-art recognition approach. Algorithms based on template update are employed for profiling systems meanwhile bidirectional recurrent neuronal networks with a Siamese training setup is used for the keystroke system. Our experiments are conducted on the semi-uncontrolled UMDAA-02 database. This database comprises smartphone sensor signals acquired during natural human-mobile interaction. Our results show that it is necessary 6 days of usage data stored to achieve the best performance in average. The template update allows to improve the equal error rate of keystroke by a relative 20%-30% performance.
引用
收藏
页码:12 / 24
页数:13
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