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P282 - BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group

https://dl.gi.de/handle/20.500.12116/23780
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Auflistung P282 - BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group nach Autor:in "Bours, Patrick"

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  • Konferenzbeitrag
    A Novel Mobilephone Application Authentication Approach based on Accelerometer and Gyroscope Data
    (BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Li, Guoqiang; Bours, Patrick
    The advent of mobile phones have changed our daily life. We are heavily relying on various applications installed on mobilephones to communicate with other, to share personal information with them, and to access our bank account, etc. However, the security measurement in terms of accessing these applications is either omitted or user-hostile because of the burden of memorizing the PIN or password. In order to relieve people from such burden, we explore the possibility of developing a mobilephone application authentication approach by analyzing the accelerometer and gyroscope data collected from the first few seconds when the user opens an application. By evaluating several proposed authentication approaches on a dataset collected from a real-life scenario, we achieve the best EER at 22.72% by only using the data collected from first 3 seconds. We think integrating the proposed non-intrusive authentication approach into the mobilephone application as an alternative for PIN/password can provide a more user-friendly authentication mechanism.
  • Konferenzbeitrag
    Predicted Templates: Learning-curve Based Template Projection for Keystroke Dynamics
    (BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Khodabakhsh, Ali; Haasnoot, Erwin; Bours, Patrick
    Keystroke Dynamics (KD) as a biometric modality can provide authentication tools in many real-life applications, virtually at zero-cost on the client side, due to the reliance of these techniques on existing hardware, and their low computational expense. One promising application is the use of KD as a second factor in password-based authentication. A downside of the existing modeling methods is the assumption of stationary behavior from the clients. However, it is expected that humans show improvements in performing a specific task following practice. In this study, we propose methods for utilization of learning models in predicting the future behavior of the clients, even with little enrollment data, and generate predicted behavioral models that can be used in different classifiers. In our experiments, the predicted templates show a reduction in the average equal-error-rate (EER) consistently across different classifiers a benchmark dataset. A reduction of 20% is achieved on the best classifier. Given fewer enrollment data, the performance gain was shown to reach above 30%. Furthermore, we show that blind detection of attacks is possible, solely relying on the global learning curve, with an EER of 16%.