Chameleons' Oblivion: Complex-Valued Deep Neural Networks for Protocol-Agnostic RF Device Fingerprinting

• Published in: 2020 IEEE European Symposium on Security and Privacy (EuroS&P) pp. 322 - 338
• Main Authors: Agadakos, Ioannis, Agadakos, Nikolaos, Polakis, Jason, Amer, Mohamed R.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2020
• Subjects: complex valued Neural Networks; Computer architecture; deep learning; device fingerprinting; device fingerprinting in the wild; Neural networks; protocol agnostic fingerprinting; Protocols; Radio frequency; radio signal fingerprinting; System performance; Telecommunication traffic; Time series analysis
• DOI: 10.1109/EuroSP48549.2020.00028

Prior work has demonstrated techniques for fingerprinting devices based on their network traffic or transmitted signals, which use software artifacts or characteristics of the underlying protocol. However these approaches are not robust or applicable in many real-world scenarios. In this paper we explore the feasibility of device fingerprinting under challenging realistic settings, by identifying artifacts in the transmitted signals caused by devices' unique hardware "imperfections". We develop RF-DCN, a novel Deep Complex-valued Neural Network (DCN) that operates on raw RF signals and is completely agnostic of the underlying applications and protocols. We introduce two DCN variations: a retrofitted Convolutional DCN (CDCN) originally created for acoustic signals, and a novel Recurrent DCN (RDCN) for modeling time series. Our work demonstrates the feasibility of operating on raw I/Q data collected within a narrowband spectrum from open air captures across vastly different modulation schemes. In contrast to prior work, we do not utilize knowledge of the modulation scheme or protocol intricacies such as carrier frequencies. We conduct an extensive experimental evaluation on large and diverse datasets as part of a DARPA red team evaluation, and investigate the effects of different environmental factors as well as neural network architectures and hyperparameters on our system's performance. Our novel RDCN consistently outperforms all baseline neural network architectures, is robust to noise, and can identify a target device even when numerous devices are concurrently transmitting within the band of interest under the same or different protocols. While our experiments demonstrate the applicability of our techniques under challenging conditions where other neural network architectures break down, we identify additional challenges in signal-based fingerprinting and provide guidelines for future explorations.