Passive Identification of WiFi Devices At-Scale: A Data-Driven Approach

WiFi has emerged as the standard method for local connectivity across various devices, including smart assistants, IoT devices, smart TVs, and AR/VR devices. Identifying WiFi devices in neighborhoods has implications for law enforcement, urban planning, and socio-economic analysis. This paper introduces a novel approach to constructing WiFi device-type signatures using Information Element attributes from wildcard WiFi probe requests. Our method accurately identifies device types even when dealing with randomized MAC addresses and requires minimal training data, thus addressing limitations of existing machine learning and deep learning approaches. We evaluate our approach using a dataset of 51,726 probe requests across 50 device types, achieving an average F1 score of 99%, precision of 99%, and recall of 98% in device-type identification. Importantly, our method outperforms deep learning methods with significantly less training data, achieving a 92% F1 score with only one training sample per device type.