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Attacks Against Mobility Prediction in 5G Networks

Atiiq, Syafiq Al LU ; Yuan, Yachao LU ; Gehrmann, Christian LU ; Sternby, Jakob LU and Barriga, Luis (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.

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author
; ; ; and
organization
publishing date
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}},
}