Evangelos Bitsikas, who is pursuing a PhD in cybersecurity at the Northwestern University in the US, applied a new machine-learning program to data gleaned from the SMS system of mobile devices.
Receiving an SMS inevitably generates Delivery Reports whose reception bestows a timing attack vector at the sender. Bitsikas developed an ML model enabling the SMS sender to determine the recipient’s location with a 96% accuracy for locations across different countries, the researcher says in a study.
The basic idea is that a hacker would send multiple text messages to the target phone, and the timing of each automated delivery reply creates a fingerprint of the target’s location. These fingerprints have ever been there but weren’t a problem until Bitsikas’ group used ML to develop an algorithm capable of reading them. They can be fed into the machine-learning model, which then responds with the predicted location.
According to the researcher, it doesn’t matter whether or not the communication is encrypted.
This type of attack theoretically also works with signal or telegram or whatever message service that works entirely without a phone number.
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