Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

Ahmed A.A. Hasan M.K. Alqahtani A. Islam S. Pandey B. Rzayeva L. Abbas H.S. Aman A.H.M. Alqahtani N.
June 2025 Tsinghua University

Tsinghua Science and Technology
2025 #30 Issue 3 1012 - 1026 pp.

Mobile devices within Fifth Generation (5G) networks, typically equipped with Android systems, serve as a bridge to connect digital gadgets such as global positioning system, mobile devices, and wireless routers, which are vital in facilitating end-user communication requirements. However, the security of Android systems has been challenged by the sensitive data involved, leading to vulnerabilities in mobile devices used in 5G networks. These vulnerabilities expose mobile devices to cyber-attacks, primarily resulting from security gaps. Zero-permission apps in Android can exploit these channels to access sensitive information, including user identities, login credentials, and geolocation data. One such attack leverages “zero-permission” sensors like accelerometers and gyroscopes, enabling attackers to gather information about the smartphones user. This underscores the importance of fortifying mobile devices against potential future attacks. Our research focuses on a new recurrent neural network prediction model, which has proved highly effective for detecting side-channel attacks in mobile devices in 5G networks. We conducted state-of-the-art comparative studies to validate our experimental approach. The results demonstrate that even a small amount of training data can accurately recognize 37.5% of previously unseen user-typed words. Moreover, our tap detection mechanism achieves a 92% accuracy rate, a crucial factor for text inference. These findings have significant practical implications, as they reinforce mobile device security in 5G networks, enhancing user privacy, and data protection.

deep learning , Fifth Generation (5G) networks , information leakage , Side-Channel Attack (SCA) , smartphone

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The Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia
Imam Alkadhim University College, Department of Computer Techniques Engineering, Baghdad, 10066, Iraq
Department of Networks and Communications Engineering, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia
The Institute of Computer Science and Digital Innovation, UCSI University, Kuala Lumpur, 56000, Malaysia
Department of Intelligent Systems and Cyber Security, Astana IT University, Astana, 20000, Kazakhstan
Department of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah, 42353, Saudi Arabia
Department of Electrical Engineering, College of Engineering, King Faisal University, Al-Hofuf, 31982, Saudi Arabia

The Center for Cyber Security
Imam Alkadhim University College
Department of Networks and Communications Engineering
The Institute of Computer Science and Digital Innovation
Department of Intelligent Systems and Cyber Security
Department of Computer Science
Department of Electrical Engineering

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