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.
I don’t think I understand the attack then. So a timing attack on Read receipts gives you approximate location how?
I understood the SMS case because the tower data could then be extrapolated. But if we’re just talking about a standard internet application like signal. The read receipts are coming over the internet and not coming from Tower records.
Or at least that’s my understanding. If I have a computer attached to some point on the internet. People could use ping timings to theoretically restrict the location but not very accurately right?
You just measure the time until the delivery recipe arrives. You can approximate how far away the recipient is. Now you keep doing that while changing your own location (use vpns etc.) and you can slowly get a more accurate location of the target. Now you automate that stuff and also utilize machine learning to interpret the data.
