| Title: |
StateFi: Effectively Identifying Wi-Fi Devices through State Transitions |
| Authors: |
Mishra, Abhishek, K; Cunche, Mathieu |
| Contributors: |
Privacy Models, Architectures and Tools for the Information Society (PRIVATICS); Centre Inria de l'Université Grenoble Alpes; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-CITI Centre of Innovation in Telecommunications and Integration of services (CITI); Institut National des Sciences Appliquées de Lyon (INSA Lyon); Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon); Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre Inria de Lyon; Institut National de Recherche en Informatique et en Automatique (Inria) |
| Source: |
StateFi: Effectively Identifying Wi-Fi Devices through State Transitions ; WISEC 2026 - 19th ACM Conference on Security and Privacy in Wireless and Mobile Networks ; https://hal.science/hal-05487604 ; WISEC 2026 - 19th ACM Conference on Security and Privacy in Wireless and Mobile Networks, Jun 2026, Saarbrücken, Germany |
| Publisher Information: |
CCSD |
| Publication Year: |
2026 |
| Collection: |
Université de Lyon: HAL |
| Subject Terms: |
[INFO]Computer Science [cs] |
| Subject Geographic: |
Saarbrücken; Germany |
| Description: |
International audience ; Randomized MAC addresses aim to prevent passive device tracking, yet Wi-Fi management frames still leak structured behavioral patterns. Prior work has relied primarily on syntactic probe-request features such as Information Elements (IEs), sequence numbers (SEQ), or RSSI correlations, which degrade in dense environments and fail under aggressive randomization. We introduce StateFi, a fingerprinting framework that models device behavior as finite-state machines (FSMs), capturing both structural transition patterns and temporal execution logic. These FSMs are embedded into compact feature vectors that support efficient similarity computation and supervised classification. Across five heterogeneous campus environments, StateFi achieves 94-97% accuracy for in-network fingerprinting using full management-frame FSMs. With probe-only FSMs, it re-identifies devices under MAC randomization with up to 97% accuracy across large public datasets comprising more than a million frames. When looking at the discrimination accuracy of the model, StateFi reaches 98%, outperforming the strongest prior signature by up to 17 percentage points. These results demonstrate that FSM-level behavioral dynamics form a powerful and largely unmitigated side channel, stable enough to defeat randomization and expressive enough for robust, scalable device identification. |
| Document Type: |
conference object |
| Language: |
English |
| Availability: |
https://hal.science/hal-05487604; https://hal.science/hal-05487604v1/document; https://hal.science/hal-05487604v1/file/StateFi.pdf |
| Rights: |
https://about.hal.science/hal-authorisation-v1/ ; info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.484AA8B2 |
| Database: |
BASE |