Tools
Tools
Xiong, Ruoting, Tong, Kit-Lun, Ren, Yi 
ORCID: https://orcid.org/0000-0001-7423-6719, Ren, Wei and Parr, Gerard ORCID: https://orcid.org/0000-0002-9365-9132
(2023)
From 5G to 6G: It is time to sniff the communications between a base station and core networks.
In:
ACM MobiCom '23.
Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM
.
Association for Computing Machinery (ACM), pp. 1478-1479.
Preview |
PDF (3570361.3614085)
- Published Version
Download (464kB) | Preview |
Abstract
Thanks to mobility and large coverage, 6G mobile networks introduce satellites and unmanned aerial vehicles as aerial base stations (ABS) in the 6G era. Instead of using a wired backhaul in 5G and its predecessor, an ABS leverages a wireless channel to a core network (CN). However, such a wireless channel design introduces new security challenges. In this paper, we present that passive attackers could sniff the ABS-CN wireless channel and identify what users are doing based on deep learning methods. We collect GTP protocol data on our testbed and use convolutional neural networks to classify 5 types of encrypted App traffic, like IG and TikTok. Experiment results proved the effectiveness of the proposed method, revealing the confidential data leakage problem on the 6G wireless ABS-CN channel.
| Item Type: | Book Section |
|---|---|
| Uncontrolled Keywords: | 6g,deep learning,encrypted data analysis,sniffing attack,wireless channel,computer networks and communications,hardware and architecture,software ,/dk/atira/pure/subjectarea/asjc/1700/1705 |
| Faculty \ School: | Faculty of Science Faculty of Science > School of Computing Sciences |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 13 Nov 2023 18:10 |
| Last Modified: | 12 Nov 2024 00:56 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/93631 |
| DOI: | 10.1145/3570361.3614085 |
Downloads
Downloads per month over past year
Actions (login required)
 |
View Item |