Dissertation
Learning of identity from behavioral biometrics for active authentication
Doctor of Philosophy (Ph.D.), Drexel University
01 Dec 2014
DOI:
https://doi.org/10.17918/etd-6338
Abstract
In this work, we look into the problem of active authentication on desktop computers and mobile devices. Active authentication is the process of continuously verifying a person's identity based on the cognitive, behavioral, and physical aspects of their interaction with the device. In this work, we consider several representative modalities including keystroke dynamics, mouse movement, application usage patterns, web browsing behavior, GPS location, and stylometry. We implement a binary classifer for each modality and organize the classifers as a parallel binary decision fusion architecture. The decisions of each classifer are fed into a decision fusion center (DFC) which applies the Chair-Varshney fusion rule to generate a global decision. The DFC minimizes the probability of error using estimates of each local classifer's false rejection rate (FAR) and false acceptance rate (FRR). We test our approach on two large datasets of 67 desktop computer users and 200 mobile device users. We are able to characterize the performance of the system with respect to intruder detection time and to quantify the contribution of each modality to the overall performance.
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Details
- Title
- Learning of identity from behavioral biometrics for active authentication
- Creators
- Lex Fridman - DU
- Contributors
- Moshe Kam (Advisor) - Drexel University (1970-)Steven P. Weber (Advisor) - Drexel University (1970-)
- Awarding Institution
- Drexel University
- Degree Awarded
- Doctor of Philosophy (Ph.D.)
- Publisher
- Drexel University; Philadelphia, Pennsylvania
- Resource Type
- Dissertation
- Language
- English
- Academic Unit
- College of Engineering (1970-2026); Electrical (and Computer) Engineering (1970-2026); Drexel University
- Other Identifier
- 6338; 991014632249004721