Guozhu Zhao1, Yuan Jiao1,*, Jianyu Zhuo1, Yu Chen1, Chuanzhen Ju1, and Yaxin Wang1
Yuan Jiao
1The School of Computer and Information Engineering, Chuzhou University, Chuzhou, Anhui 239000, China
*Corresponding author
As smartphones become essential tools for daily activities, security concerns, particularly in mobile payments and personal privacy, are becoming increasingly critical. Traditional lock screens and password-based authentication systems are vulnerable to various threats, such as unauthorized access and identity theft. To address these issues, this paper proposes an advanced continuous authentication method that verifies user identity based on unique screen-touch trajectories. Using machine learning algorithms, the authentication system analyzes real-time screen-touch trajectories from user interactions with the touchscreen, capturing behavioral patterns that are highly specific to individual users. This proposed continuous authentication framework enables the system to authenticate users seamlessly and continuously throughout device usage, ensuring ongoing protection against unauthorized access, financial fraud, and privacy breaches. Experimental results demonstrate that the Random Forest algorithm outperforms other methods in terms of recognition accuracy and response efficiency, offering a reliable and scalable solution. The paper also explores the integration of this authentication approach into existing smartphone security frameworks, showcasing its potential to significantly enhance network security, particularly in high-risk applications such as mobile payments and personal data access.
Continuous authentication, mobile payments and personal privacy, screen-touch trajectories, smartphone security frameworks, payment security
Guozhu Zhao, Yuan Jiao, Jianyu Zhuo, Yu Chen, Chuanzhen Ju, and Yaxin Wang (2024). Continuous Authentication of Smartphones Based on Screen-Touch Trajectories. Journal of Networking and Network Applications, Volume 4, Issue 3, pp. 102–108. https://doi.org/10.33969/J-NaNA.2024.040301.
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