Exploring touch-based behavioral authentication on smartphone email applications in IoT-enabled smart cities

Li, Wenjuan; Meng, Weizhi; Furnell, Steven

Abstract

© 2021 Elsevier B.V. The Internet of Things (IoT) allows various embedded devices and smart sensors to be connected with each other, which provides a basis for building smart cities. The IoT-enabled smart city can greatly benefit people's daily lives, where smartphone is one of the most widely used IoT devices. For example, people can use the phone to check their financial account, store personal data and communicate with peers. Thus it is very important to safeguard the phones from unauthorized access. To complement traditional textual passwords, touch behavioral authentication has attracted much attention while it is still a challenge on how to build a robust scheme in practice. This is because users’ touch actions are often dynamic and hard to model. For this challenge, previous work has proved that touch actions could become consistent when users interact with social networking applications. Motivated by this observation, in this work, we perform a study to investigate users’ touch behavior within Email applications on smartphones (with Email being one of the most important and widely used means in connecting with others). The study results with 60 participants validate the former observation that users’ touch behavioral deviation can be greatly decreased when they play Email applications.

Citation

Li, W., Meng, W., & Furnell, S. (2021). Exploring touch-based behavioral authentication on smartphone email applications in IoT-enabled smart cities. Pattern Recognition Letters, 144, 35-41. https://doi.org/10.1016/j.patrec.2021.01.019

• Journal Article Type: Article
• Acceptance Date: Jan 18, 2021
• Online Publication Date: Jan 20, 2021
• Publication Date: Apr 1, 2021
• Deposit Date: Jan 29, 2021
• Publicly Available Date: Jan 21, 2022
• Journal: Pattern Recognition Letters
• Print ISSN: 0167-8655
• Electronic ISSN: 1872-7344
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 144
• Pages: 35-41
• DOI: https://doi.org/10.1016/j.patrec.2021.01.019
• Keywords: Signal Processing; Software; Artificial Intelligence; Computer Vision and Pattern Recognition
• Public URL: https://nottingham-repository.worktribe.com/output/5262755
• Publisher URL: https://www.sciencedirect.com/science/article/abs/pii/S0167865521000325
• Additional Information: This article was published in Pattern Recognition Letters, 144, Li, W., Meng, W., & Furnell, S., Exploring touch-based behavioral authentication on smartphone email applications in IoT-enabled smart cities, 35–41, Copyright Elsevier (2021).