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.

翻译：探测请求是设备在网络发现过程中周期性发送的Wi-Fi管理帧。长期追踪探测请求可揭示移动模式、流量动态与行为趋势，这些信息在城市规划、人类移动性分析及零售分析等应用中至关重要。为保护用户隐私，业界采用如MAC地址随机化等技术，通过周期性变更设备MAC地址以限制追踪。然而研究表明，此类隐私保护措施仍可被规避。通过分析探测请求帧内信息元素，可实现设备指纹识别与长期用户追踪。本文提出一种基于机器学习的紧凑型Wi-Fi探测请求指纹识别方法。我们采用非对称成对提升算法学习判别性滤波器，用于处理探测请求帧中的特定比特序列，并将处理结果量化为紧凑的二进制格式。在公开数据集上的大量实验表明，本方法在保持鲁棒指纹识别性能的同时，相比现有方案实现了两个数量级的存储空间压缩。