USENIX Security '23 - Freaky Leaky SMS: Extracting User Locations by Analyzing SMS Timings

USENIX Security '23 - Freaky Leaky SMS: Extracting User Locations by Analyzing SMS Timings

Evangelos Bitsikas, Northeastern University; Theodor Schnitzler, Research Center Trustworthy Data Science and Security; Christina Pöpper, New York University Abu Dhabi; Aanjhan Ranganathan, Northeastern University

Short Message Service (SMS) remains one of the most popular communication channels since its introduction in 2G cellular networks. In this paper, we demonstrate that merely receiving silent SMS messages regularly opens a stealthy side-channel that allows other regular network users to infer the whereabouts of the SMS recipient. The core idea is that receiving an SMS inevitably generates Delivery Reports whose reception bestows a timing attack vector at the sender. We conducted experiments across various countries, operators, and devices to show that an attacker can deduce the location of an SMS recipient by analyzing timing measurements from typical receiver locations. Our results show that, after training an ML model, the SMS sender can accurately determine multiple locations of the recipient. For example, our model achieves up to 96% accuracy for locations across different countries, and 86% for two locations within Belgium. Due to the way cellular networks are designed, it is difficult to prevent Delivery Reports from being returned to the originator making it challenging to thwart this covert attack without making fundamental changes to the network architecture.

View the full USENIX Security '23 program at https://www.usenix.org/conference/use...