Multi-Class Device Identification on Large-Scale WiFi Network Traffic Data
2025 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2025, Chisinau, Moldova, 23 - 26 Haziran 2025, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/blackseacom65655.2025.11193905
- Basıldığı Şehir: Chisinau
- Basıldığı Ülke: Moldova
- Anahtar Kelimeler: device fingerprinting, Device identification, multi-class, network traffic data, WiFi analytics
- Boğaziçi Üniversitesi Adresli: Evet
Özet
We present a large-scale device identification study for six classes using over 110 million anonymized time series WiFi traffic data points from 11,513 unique devices. Through an ablation consisting of 44 ensemble machine learning models with differing modeling and featurization approaches, we demonstrate that Random Forest classifiers leveraging rich temporal features achieve strong multi-class performance (F1-macro: 0.77 validation, 0.68 test), from which the device's transmission throughput and received signal strength features emerge as most important.