Saturday, November 26, 2022

Bluetooth signal can be used to identify and track smartphones

Bluetooth Fingerprint Detection On University Campus

Image: Researchers test their method for tracking Bluetooth fingerprints on campus. They use an off-the-shelf device to track and identify devices.
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credit: University of California San Diego

A team of engineers at the University of California San Diego has demonstrated for the first time that the Bluetooth signal continuously emitted by our mobile phones contains a unique fingerprint that can be used to track the movements of individuals.

Mobile devices, including phones, smartwatches and fitness trackers, transmit signals continuously at a rate of about 500 beacons per minute, known as Bluetooth beacons. These beacons enable features such as Apple’s “Find My” lost device tracking service; COVID-19 tracing apps; And connect smartphones to other devices like wireless earphones.

Earlier research has shown that wireless fingerprinting is present in WiFi and other wireless technologies. The key insight from the UC San Diego team was that this form of tracking can also be done with Bluetooth in a highly accurate manner.

“This is important as Bluetooth has become a more significant threat in today’s world as it is the persistent and persistent wireless signal emitted by all of our personal mobile devices,” said Nishant Bhaskar, a Ph.D. he said. student in the UC San Diego Department of Computer Science and Engineering and one of the paper’s lead authors.

The team, which includes researchers from the Department of Computer Science and Engineering and Electrical and Computer Engineering, presented their findings on May 24, 2022, at the IEEE Security and Privacy Conference in Oakland, Calif.

All wireless devices have minor manufacturing flaws in the hardware that are unique to each device. These fingerprints are an accidental byproduct of the manufacturing process. These flaws in Bluetooth hardware result in unique distortions, which can be used as fingerprints to track a specific device. For Bluetooth, this would allow an attacker to circumvent anti-tracking techniques such as constantly changing the address used by mobile devices to connect to Internet networks.

Tracking individual devices via bluetooth is not easy. The prior fingerprinting technology built for WiFi relies on the fact that the WiFi signal consists of a long-known sequence, called a preamble. But the preamp is extremely short for a Bluetooth beacon signal.

“The short duration gives a false fingerprint, which makes prior techniques for Bluetooth tracking not useful,” said Hadi Givechian, a UC San Diego computer science Ph.D. student and a lead author on the paper.

Instead, the researchers devised a new method that does not rely on the preamble but looks at the entire Bluetooth signal. They developed an algorithm that approximates two different values ​​found in a Bluetooth signal. These values ​​vary depending on defects in the Bluetooth hardware, giving the researchers a unique fingerprint of the device.

real world experiments

The researchers evaluated their tracking method through several real-world experiments. In the first experiment, they found that 40% of 162 mobile devices seen in public areas, for example coffee shops, were uniquely identifiable. Next, they scaled the experiment and observed 647 mobile devices in public hallways over two days. The team found that 47% of these devices had unique fingerprints. Finally, the researchers performed a real tracking attack by fingerprinting and following a study volunteer-owned mobile device as they moved in and out of their home.


Although their discovery is concerning, the researchers also discovered a number of challenges that an attacker would face in practice. For example, a change in ambient temperature can change the Bluetooth fingerprint. Some devices also send Bluetooth signals with varying degrees of power, and this affects the distance at which these devices can be tracked.

The researchers also note that their method requires an attacker to have a high level of expertise, so it is unlikely to pose a widespread threat to the public today.

Despite the challenges, the researchers found that Bluetooth tracking is possible for a large number of devices. It also doesn’t require sophisticated equipment: the attack can be carried out with equipment that costs less than $200.

Solutions and next steps

So how can the problem be fixed? Basically, the Bluetooth hardware has to be redesigned and replaced. But the researchers believe that other, easier solutions may be discovered. The team is currently working on a way to hide the Bluetooth fingerprint through digital signal processing in the Bluetooth device firmware.

The researchers are also exploring whether the method they developed can be applied to other types of devices. “Every form of communication today is wireless, and is at risk,” said Dinesh Bharadia, a professor in the UC San Diego Department of Electrical and Computer Engineering and one of the paper’s senior authors. “We are working to build hardware-level protection against potential attacks.”

The researchers observed that simply disabling Bluetooth may not cause all phones to stop emitting Bluetooth beacons. For example, the home screen of some Apple devices emit beacons even when Bluetooth is turned off from the Control Center. “As far as we know, the only thing that definitely stops the Bluetooth beacon is your phone being turned off,” Bhaskar said.

The researchers are careful to say that even though they can track individual devices, they haven’t been able to obtain any information about the device’s owners. The study was reviewed by the campus’ internal review board and campus counsel.

“It’s really the tools that are under investigation,” said Aaron Schulman, a UC San Diego computer science professor and one of the paper’s senior authors.

Evaluation of physical-layer BLE location tracking attacks on mobile devices

Dinesh Bharadia, UC San Diego Department of Electrical and Computer Engineering

Nishant Bhaskar, Hadi Givenchian, Aaron Shulman, UC San Diego Department of Computer Science and Engineering

Christian Demuff, UC San Diego Department of Emergency Medicine

Eliana Rodriguez Herrera Hector Rodrigo López Soto, UC San Diego Enlace Program

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