Attacks Against Mobility Prediction in 5G Networks
(2023) 22nd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2023 p.1502-1511- Abstract
The 5th generation of mobile networks introduces a new Network Function (NF) that was not present in previous generations, namely the Network Data Analytics Function (NWDAF). Its primary objective is to provide advanced analytics services to various entities within the network and also towards external application services in the 5G ecosystem. One of the key use cases of NWDAF is mobility trajectory prediction, which aims to accurately support efficient mobility management of User Equipment (UE) in the network by allocating "just in time"necessary network resources. In this paper, we show that there are potential mobility attacks that can compromise the accuracy of these predictions. In a semi-realistic scenario with 10,000... (More)
The 5th generation of mobile networks introduces a new Network Function (NF) that was not present in previous generations, namely the Network Data Analytics Function (NWDAF). Its primary objective is to provide advanced analytics services to various entities within the network and also towards external application services in the 5G ecosystem. One of the key use cases of NWDAF is mobility trajectory prediction, which aims to accurately support efficient mobility management of User Equipment (UE) in the network by allocating "just in time"necessary network resources. In this paper, we show that there are potential mobility attacks that can compromise the accuracy of these predictions. In a semi-realistic scenario with 10,000 subscribers, we demonstrate that an adversary equipped with the ability to hijack cellular mobile devices and clone them can significantly reduce the prediction accuracy from 75% to 40% using just 100 adversarial UEs. While a defense mechanism largely depends on the attack and the mobility types in a particular area, we prove that a basic KMeans clustering is effective in distinguishing legitimate and adversarial UEs.
(Less)
- author
- Atiiq, Syafiq Al LU ; Yuan, Yachao LU ; Gehrmann, Christian LU ; Sternby, Jakob LU and Barriga, Luis
- organization
- publishing date
- 2023
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- 5G, adversarial mobility, mobility prediction, NWDAF
- host publication
- Proceedings - 2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom/BigDataSE/CSE/EUC/iSCI 2023
- editor
- Hu, Jia ; Min, Geyong and Wang, Guojun
- pages
- 10 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 22nd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2023
- conference location
- Exeter, United Kingdom
- conference dates
- 2023-11-01 - 2023-11-03
- external identifiers
-
- scopus:85195520991
- ISBN
- 9798350381993
- DOI
- 10.1109/TrustCom60117.2023.00205
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2023 IEEE.
- id
- cbee3692-60dc-4c1b-823c-2c8dcd7cf70e
- date added to LUP
- 2024-06-19 16:08:48
- date last changed
- 2024-06-20 11:51:20
@inproceedings{cbee3692-60dc-4c1b-823c-2c8dcd7cf70e, abstract = {{<p>The 5<sup>th</sup> generation of mobile networks introduces a new Network Function (NF) that was not present in previous generations, namely the Network Data Analytics Function (NWDAF). Its primary objective is to provide advanced analytics services to various entities within the network and also towards external application services in the 5G ecosystem. One of the key use cases of NWDAF is mobility trajectory prediction, which aims to accurately support efficient mobility management of User Equipment (UE) in the network by allocating "just in time"necessary network resources. In this paper, we show that there are potential mobility attacks that can compromise the accuracy of these predictions. In a semi-realistic scenario with 10,000 subscribers, we demonstrate that an adversary equipped with the ability to hijack cellular mobile devices and clone them can significantly reduce the prediction accuracy from 75% to 40% using just 100 adversarial UEs. While a defense mechanism largely depends on the attack and the mobility types in a particular area, we prove that a basic KMeans clustering is effective in distinguishing legitimate and adversarial UEs.</p>}}, author = {{Atiiq, Syafiq Al and Yuan, Yachao and Gehrmann, Christian and Sternby, Jakob and Barriga, Luis}}, booktitle = {{Proceedings - 2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom/BigDataSE/CSE/EUC/iSCI 2023}}, editor = {{Hu, Jia and Min, Geyong and Wang, Guojun}}, isbn = {{9798350381993}}, keywords = {{5G; adversarial mobility; mobility prediction; NWDAF}}, language = {{eng}}, pages = {{1502--1511}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Attacks Against Mobility Prediction in 5G Networks}}, url = {{http://dx.doi.org/10.1109/TrustCom60117.2023.00205}}, doi = {{10.1109/TrustCom60117.2023.00205}}, year = {{2023}}, }