Current software for user authentication relies on the user to directly initiate some interaction (i.e., active authentication). However, active authentication systems are not accessible to individuals across all age groups. Continuous authentication schemes transparently observe a user's natural multimodal behaviors to leverage all possible signals as input for authentication, and hence do not require explicit authentication interactions to be initiated by the user, and are thus a promising framework for authentication by individuals of different age groups. This project's novelties are 1) to advance understanding of how individuals of different age groups use and perceive existing authentication methods, especially concerning users' mental models and acceptance of monitoring for the purposes of continuous authentication, and 2) to collect and analyze a variety of user signals in multiple behavioral and physiological modalities for age-aware continuous authentication on personal computing devices. This research also informs the design of continuous authentication interactions in other contexts such as public spaces and other smart environments, in which continuous authentication might be useful. The research includes three phases. (1) Elicit the mental models multi-generational users have of what it means to authenticate to a system, if and when they expect the system to re-authenticate them to confirm their identity as they continue to interact, and if and how they expect to receive feedback of authentication attempts. (2) Produce a novel dataset of behavioral and physiological data, such as touch gestures, keystroke dynamics, heart-rate variability, and skin temperature, through a series of data collection sessions wherein individuals of different age groups will be recruited to complete a diverse set of tasks. (3) Develop fundamental knowledge of age-aware continuous authentication through the analysis of these data using state-of-the-art machine and deep learning techniques.
- Meghna Chaudhary
- Sandeep Lakshminarayan
- Liza Jivnani
- Nicolas Ng Wai
- Tara Nourivandi
- Kevin Antony
- Orestes Bringas
- Param Chokshi
- Dr. Tempestt Neal, Cyber Identity and Behavior Research Lab, University of South Florida, PI
- Dr. Shaun Canavan, Affective Vision Lab, University of South Florida, Co-PI
- Dr. Lisa Anthony, Intelligent Natural Interaction Technology Lab, University of Florida, Co-PI
- Dr. Jaime Ruiz, Ruiz Human-Computer Interaction Lab, University of Florida, Co-PI
This material is based upon work supported by the National Science Foundation under Grant No. 2039373 and 2039379. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.