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Open Access

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

Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia, and also with Imam Alkadhim University College, Department of Computer Techniques Engineering, Baghdad 10066, Iraq
Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
Department of Networks and Communications Engineering, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
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, Kazakstan
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
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Abstract

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 smartphone’s 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.

References

[1]

A. T. Le, T. T. Hoang, B. A. Dao, A. Tsukamoto, K. Suzaki, and C. K. Pham, A real-time cache side-channel attack detection system on RISC-V out-of-order processor, IEEE Access, vol. 9, pp. 164597–164612, 2021.

[2]

A. A. Ahmed, R. A. Salim, and M. K. Hasan, Deep learning method for power side-channel analysis on chip leakages, Elektron. Elektrotech., vol. 29, no. 6, pp. 50–57, 2023.

[3]

J. Ma and J. Hu, Safe consensus control of cooperative-competitive multi-agent systems via differential privacy, Kybernetika, vol. 58, no. 3, pp. 426–439, 2022.

[4]

A. A. Ahmed, M. K. Hasan, I. Memon, A. H. M. Aman, S. Islam, T. R. Gadekallu, and S. A. Memon, Secure AI for 6G mobile devices: Deep learning optimization against side-channel attacks, IEEE Trans. Consum. Electron., vol. 70, no. 1, pp. 3951–3959, 2024.

[5]

A. K. Jakkani, P. Reddy, and J. Jhurani, Design of a novel deep learning methodology for IoT botnet based attack detection, Int. J. Recent Innov. Trends Comput. Commun., vol. 11, no. 9, pp. 4922–4927, 2023.

[6]
A. A. Amjed, K. H. Mohammad, A. M. N. Shahrul, H. A. Azana, Design of time-delay convolutional neural networks (TDCNN) model for feature extraction for side-channel attacks, Int. J. Comput. Digital Syst., vol. 16, no. 1, pp. 341–351, 2024.
[7]

H. Jiang, M. Wang, P. Zhao, Z. Xiao, and S. Dustdar, A utility-aware general framework with quantifiable privacy preservation for destination prediction in LBSs, IEEE/ACM Trans. Netw., vol. 29, no. 5, pp. 2228–2241, 2021.

[8]

J. Yu, L. Lu, Y. Chen, Y. Zhu, and L. Kong, An indirect eavesdropping attack of keystrokes on touch screen through acoustic sensing, IEEE Trans. Mob. Comput., vol. 20, no. 2, pp. 337–351, 2021.

[9]
Y. Liu, B. Zhao, Z. Zhao, J. Liu, X. Lin, Q. Wu, and W. Susilo, SS-DID: A secure and scalable Web3 decentralized identity utilizing multi-layer sharding blockchain, IEEE Internet Things J., vol. 11, no. 15, pp. 25694−25705, 2024.
[10]
Z. Wu, G. Liu, J. Wu, and Y. Tan, Are neighbors alike? A semisupervised probabilistic collaborative learning model for online review spammers detection, Inform. Syst. Res., doi: 10.1287/isre.2022.0047.
[11]
C. Shen, C. Chen, and J. Zhang, 2021. Micro-architectural cache side-channel attacks and countermeasures, in Proc. 26th Asia and South Pacific Design Automation Conference (ASPDAC ’21 ), Association for Computing Machinery, New York, NY, USA, pp. 441–448.
[12]
A. P. Felt, M. Finifter, E. Chin, S. Hanna, and D. Wagner, A survey of mobile malware in the wild, in Proc. 1 st ACM Workshop on Security and Privacy in Smartphones and Mobile Devices, Chicago, IL, USA, 2011, pp. 3–14.
[13]
M. Mehrnezhad, J. Harrison, and T. Ehsan, A practical deep learning-based acoustic side channel attack on keyboards, in 2023 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW ), Delft, The Netherlands, 2023.
[14]

N. Gao, Y. Han, N. Li, S. Jin, and M. Matthaiou, When physical layer key generation meets RIS: Opportunities, challenges, and road ahead, IEEE Wirel. Commun., vol. 31, no. 3, pp. 355–361, 2024.

[15]

S. Mekruksavanich and A. Jitpattanakul, Deep learning approaches for continuous authentication based on activity patterns using mobile sensing, Sensors, vol. 21, no. 22, p. 7519, 2021.

[16]

L. Zhao, S. Qu, H. Xu, Z. Wei, and C. Zhang, Energy-efficient trajectory design for secure SWIPT systems assisted by UAV-IRS, Veh. Commun., vol. 45, p. 100725, 2024.

[17]
M. Mushtaq, J. Bricq, M. K. Bhatti, A. Akram, V. Lapotre, G. Gogniat, and P. Benoit, WHISPER: A tool for run-time detection of side-channel attacks, IEEE Access, vol. 8, pp. 83871–83900, 2020.
[18]

Y. Ou and L. Li, Side-channel analysis attacks based on deep learning network, Front. Comput. Sci., vol. 16, no. 2, p. 162303, 2022.

[19]

H. Pearce, V. R. Surabhi, P. Krishnamurthy, J. Trujillo, R. Karri, and F. Khorrami, Detecting hardware Trojans in PCBs using side channel loopbacks, IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 30, no. 7, pp. 926–937, 2022.

[20]

S. Picek, G. Perin, L. Mariot, L. Wu, and L. Batina, SoK: Deep learning-based physical side-channel analysis, ACM Comput. Surveys, vol. 55, no. 11, p. 227, 2023.

[21]

X. Liu, S. Wang, S. Lu, Z. Yin, X. Li, L. Yin, J. Tian, and W. Zheng, Adapting feature selection algorithms for the classification of Chinese texts, Systems, vol. 11, no. 9, p. 483, 2023.

[22]
N. Ludant, and G. Noubir, SigUnder: A stealthy 5G low power attack and defenses, in Proc. 14 th ACM Conf. Security and Privacy in Wireless and Mobile Networks, Abu Dhabi, United Arab Emirates, 2021, pp. 250–260.
[23]

M. M. Kataa and W. Kaur, Recognizing facial emotion in real-time using MuWNet a novel deep learning network, 2024, Asia-Pacific Journal of Information Technology and Multimedia, vol. 13, no. 1, pp. 1–20, 2024.

[24]

J. Zhu, F. Li, and J. Chen, A survey of blockchain, artificial intelligence, and edge computing for Web 3.0, Computer Science Review, vol. 54, p. 100667, 2024.

[25]

M. Li, H. Cui, C. Liu, C. Shan, X. Du, and M. Guizani, A four-dimensional space-based data multi-embedding mechanism for network services, IEEE Trans. Netw. Serv. Manag., vol. 21, no. 3, pp. 2741–2750, 2024.

[26]
Y. Li, Y. Luo, X. Wu, Z. Shi, S. Ma, and G. Yang, Variational Bayesian learning based localization and channel reconstruction in RIS-aided systems, IEEE Trans. Wirel. Commun., doi: 10.1109/TWC.2024.3380903.
[27]

H. Al-Aqrabi, A. P. Johnson, R. Hill, P. Lane, and T. Alsboui, Hardware-intrinsic multi-layer security: A new frontier for 5G enabled IIoT, Sensors, vol. 20, no. 7, p. 1963, 2020.

[28]

P. Kocher, J. Jaffe, B. Jun, and P. Rohatgi et al., Introduction to differential power analysis, J. Cryptogr. Eng., vol. 1, pp. 5–27, 2011.

[29]
A. A. Ahmed, M. K. Hasan, N. S. M. Satar, N. S. Nafi, A. H. Aman, S. Islam, and S. A. Fadhil, Detection of crucial power side channel data leakage in neural networks, in Proc. 2023 33 rd Int. Telecommunication Networks and Applications Conf., Melbourne, Australia, 2023, pp. 57–62.
[30]
A. A. Ahmed, M. K. Hasan, N. S. Nafi, A. H. Aman, S. Islam, and S. A. Fadhil, Design of lightweight cryptography based deep learning model for side channel attacks, in Proc. 2023 33 rd Int. Telecommunication Networks and Applications Conf., Melbourne, Australia, 2023, pp. 325–328.
[31]
B. John, Algorithm-agnostic system for measuring susceptibility of cryptographic accelerators to power side channel attacks, Master dissertation, Massachusetts Institute of Technology, Cambridge, MA, USA, 2022.
[32]

M. Yang, T. Ahmed, S. Inagaki, K. Sakiyama, Y. Li, and Y. Hara-Azumi, Hardware/software cooperative design against power side-channel attacks on IoT devices, IEEE Internet Things J., vol. 11, no. 9, pp. 16758–16768, 2024.

[33]

A. R. Javed, M. O. Beg, M. Asim, T. Baker, and A. H. Al-Bayatti, AlphaLogger: Detecting motion-based side-channel attack using smartphone keystrokes, J. Ambient. Intell. Humaniz. Comput., vol. 14, no. 5, pp. 4869–4882, 2023.

[34]

T. Deng, H. Wang, D. He, N. Xiong, W. Liang, and J. Wang, Multi-dimensional fusion deep learning for side channel analysis, Electronics, vol. 12, no. 23, p. 4728, 2023.

[35]

L. Zhang, X. Xing, J. Fan, Z. Wang and S. Wang, Multilabel deep learning-based side-channel attack, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 40, no. 6, pp. 1207–1216, 2021.

Tsinghua Science and Technology
Pages 1012-1026
Cite this article:
Ahmed AA, Hasan MK, Alqahtani A, et al. Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks. Tsinghua Science and Technology, 2025, 30(3): 1012-1026. https://doi.org/10.26599/TST.2024.9010123

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Received: 18 March 2024
Revised: 25 June 2024
Accepted: 01 July 2024
Published: 30 December 2024
© The Author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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