Compact Probe Request Fingerprinting with Asymmetric Pairwise Boosting

TL;DR

This paper introduces a machine learning method using Asymmetric Pairwise Boosting to create compact, efficient fingerprints of Wi-Fi Probe Requests, enabling effective device tracking while significantly reducing storage requirements.

Contribution

It proposes a novel, compact fingerprinting technique for Probe Requests that outperforms existing methods in storage efficiency without sacrificing accuracy.

Findings

Achieves two orders of magnitude reduction in storage compared to previous methods.

Maintains high fingerprinting accuracy with the compact representation.

Demonstrates robustness on public datasets.

Abstract

Probe Requests are Wi-Fi management frames periodically sent by devices during network discovery. Tracking Probe Requests over time offers insights into movement patterns, traffic flows, and behavior trends, which are keys in applications such as urban planning, human mobility analysis, and retail analytics. To protect user privacy, techniques such as MAC address randomization are employed, periodically altering device MAC addresses to limit tracking. However, research has shown that these privacy measures can be circumvented. By analyzing the Information Elements (IE) within the Probe Request body, it is possible to fingerprint devices and track users over time. This paper presents a machine learning-based approach for fingerprinting Wi-Fi Probe Requests in a compact fashion. We utilize Asymmetric Pairwise Boosting to learn discriminating filters which are then used to process specific bit sequences in Probe Request frames, and quantize the results into a compact binary format. Extensive evaluation on public datasets demonstrates a two-order-of-magnitude storage reduction compared to existing methods while maintaining robust fingerprinting performance.