The rapid and ubiquitous adoption of mobile device use has propagated our dependence on their ability to keep individuals within our society connected. Mobile devices are nowadays used as the major way of communication and connecting to the internet for many people, almost all of them are not computer professionals. As with any technology with wide-adoption, many challenges have come to the fact for this area as well. Due to the nature of mobile communication, data transmission is the fundamental method of connecting users on the network. As with any form of data transmission, data security is a key concern which must be taken into account. Several methods of user authentication and authorization exist for the purpose of privacy preservation and security and are widely used in mobile systems. One such method is the Continuous Proof of Presence (CPoP) authentication, which has the potential to provide an extra layer of security to users in data sensitive industries, such as the security sector, government and corporate administration, and healthcare. In this work we present TouchMetric, a mobile application developed for Android and iOS, used for the purpose of testing a machine learning model for the development of a CPoP feature.
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This work is partially supported by the School of Computer Science, University of Windsor, Natural Sciences and Engineering Research Council of Canada (NSERC), and in-kind contribution from our industry partner and project owner, Plurilock Inc.
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Samet, S., Ishraque, M.T., Ghadamyari, M. et al. TouchMetric: a machine learning based continuous authentication feature testing mobile application. Int. j. inf. tecnol. 11, 625–631 (2019). https://doi.org/10.1007/s41870-019-00306-w
- Data privacy
- Continuous authentication
- Biometric authentication
- Identity management
- Mobile security