Attacks Against Mobility Prediction in 5G Networks
Syafiq Al Atiiq Yachao Yuan Christian Gehrmann Jakob Sternby Luis Barriga
The $5^{th}$ 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.