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.
Silent SMS are working as designed. There is a reason they are called silent.
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This is another excellent reason to never give anyone at all your cell phone number. Give them a voice number, like Google voice, Google Fi, voip.ms. The number of people have should not be the number attached to the device you walk around with.
Then if somebody wants to track you by your phone number they’ll have to go to the phone service who is not connected directly to your phone other than through the internet. And then they’ll have to track you through the internet. So it won’t be a data broker selling your location data enmass indexable by your known phone number.
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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