The goal of this paper is to assess how different User Terminals react to IMSI-catching attacks, namely location privacy attacks aiming at gathering the user's International Mobile Subscriber Identity (IMSI). After having implemented two different attack techniques over two different Software-Defined-Radio (SDR) platforms (OpenAirInterface and srsLTE), we have tested these attacks over different versions of the mobile phone brands, for a total of 19 different radio modems tested. We show that while the majority of devices surrender almost immediately, iPhones seem to implement some cleverness that resembles proper countermeasures. We also bring about evidence that the two chosen SDR platforms implement different signaling procedures that differentiate their ability as IMSI-catchers. We finally analyse IMSI-catchers' behaviors against subscribers of different operators, showing that successfulness of the attack depends only on the chipset and the SDR tool. We believe that our analysis may be useful either to practitioners that need to experiment with mobile security, as well as engineers for improving the design of mobile modems.
CCS CONCEPTS• Security and privacy → Network security; Mobile and wireless security;
Most localization systems rely on measurements gathered from signals emitted by stations whose position is assumed known as ground truth, namely anchors. As demonstrated by a significant bulk of experimental research, location security is threatened when an attacker becomes able to tamper either the signals emitted by the stations, or convince the user that the anchor station is in a different position than the true one. With this paper, we first propose a formal threat model which captures the above-mentioned wide class of attacks, and permits to quantitatively evaluate how tampering of one or more anchor locations undermines the user's localization accuracy. We specifically derive a Cramér Rao Bound for the localization error, and we assess a number of example scenarios. We believe that our study may provide a useful formal benchmark for the design and analysis of detection and mitigation solutions.
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