  Researchers have demonstrated various techniques for fingerprinting and
identifying devices. Previous approaches have identified devices from their
network traffic or transmitted signals while relying on software or operating
system specific artifacts (e.g., predictability of protocol header fields) or
characteristics of the underlying protocol (e.g.,frequency offset). As these
constraints can be a hindrance in real-world settings, we introduce a
practical, generalizable approach that offers significant operational value for
a variety of scenarios, including as an additional factor of authentication for
preventing impersonation attacks. Our goal is to identify artifacts in
transmitted signals that are caused by a device's unique hardware
"imperfections" without any knowledge about the nature of the signal. 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 present two DCN variations: (i) Convolutional DCN (CDCN) for
modeling full signals, and (ii) Recurrent DCN (RDCN) for modeling time series.
Our system handles raw I/Q data from open air captures within a given spectrum
window, without knowledge of the modulation scheme or even the carrier
frequencies. While our experiments demonstrate the effectiveness of our system,
especially under challenging conditions where other neural network
architectures break down, we identify additional challenges in signal-based
fingerprinting and provide guidelines for future explorations. Our work lays
the foundation for more research within this vast and challenging space by
establishing fundamental directions for using raw RF I/Q data in novel
complex-valued networks.
