Analysis of Wi-Fi Probe Requests Towards Information Element Fingerprinting

In the past decade, several algorithms have been proposed to monitor people's mobility based on the analysis of management messages generated by Wi-Fi devices and which rely on the factory physical addresses to identify the source. However, since 2012, major mobile device manufacturers have started protecting their clients' privacy through non-reversible encryption of these identifiers and the omission of other infor-mation. To still protect user privacy and at the same time allow for the identification of frames generated by the same source, we have conducted an extensive analysis of the major fields of these messages, which are called Information Elements. To this, we have analysed an open dataset of Probe Requests sent by individual devices that were captured in isolated or pseudo-isolated environments. In the first part of our analysis, we used the Random Forest algorithm to evaluate the importance of Information Elements for the clustering of Probe Requests, and we discovered that three of them are more valuable than the others. By exploiting this outcome, we implemented a clustering algorithm and found the best settings which allowed us to achieve the correct Probe Requests clustering on average in 92% of cases.