Short: Defeating MAC Address Randomization Through Timing Attacks

CĂ©lestin Matte, Matthieu Cunche, Franck Rousseau, Mathy Vanhoef

MAC address randomization is a common privacy protection measure deployed in major operating systems today. It is used to prevent user-tracking with probe requests that are transmitted during IEEE 802.11 network scans. We present an attack to defeat MAC address randomization through observation of the timings of the network scans with an off-the-shelf Wi-Fi interface. This attack relies on a signature based on inter-frame arrival times of probe requests, which is used to group together frames coming from the same device although they use distinct MAC addresses. We propose several distance metrics based on timing and use them together with an incremental learning algorithm in order to group frames. We show that these signatures are consistent over time and can be used as a pseudo-identifier to track devices. Our framework is able to correctly group frames using different MAC addresses but belonging to the same device in up to 75% of the cases. These results show that the timing of 802.11 probe frames can be abused to track individual devices and that address randomization alone is not always enough to protect users against tracking.

This short paper presents a method to identify devices despite MAC address randomization through the observation of network scan timings. This method can be implemented with an off-the-shelf Wi-Fi interface and uses a device signature based on inter-frame arrival times of probe requests. This signature can be used to group together frames coming from the same device even though these use different MAC addresses.

The reviewers appreciated the problem, particularly given that MAC randomization is still a relatively new countermeasure. They also liked how the authors clearly outlined the proposed method while providing valuable insight. The initial results along with the discussion on the effectiveness of different distance metrics are promising. The reviewers encourage this preliminary work to be developed further, especially with regards to 1) a larger and better defined data set, 2) a better explanation of how signature are constructed and parameters are chosen for realistic network conditions, and 3) more detailed evaluation and further discussion of the results.