Device Type Classification Using WiFi Probe Requests: From Signals to Insights

WiFi devices are ubiquitous in modern environments, from smartphones and laptops to IoT sensors and AR/VR headsets. Identifying device types/models within these populations enables crowd analysis, network optimization, and detection of unusual devices. Current identification methods struggle with MAC address randomization, require large training datasets, and perform poorly in real-world deployments. This paper introduces a device identification method based on Information Element (IE) attributes extracted from WiFi probe requests. We evaluate the approach using probe requests captured in the 2.4 GHz band. Evaluation across 70+ device types yields 99% precision, 98% recall, and 99% F1 score, exceeding deep learning approaches (92% F1 score) under similar training conditions. Our approach maintains accuracy despite MAC randomization and requires minimal training data. We demonstrate practical applicability through an operational dashboard tested in real-world scenarios for urban planning and network management. Case studies across diverse environments confirm the effectiveness of the method for operational use.