English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Researchers have demonstrated various techniques for fingerprinting andidentifying devices. Previous approaches have identified devices from theirnetwork traffic or transmitted signals while relying on software or operatingsystem specific artifacts (e.g., predictability of protocol header fields) orcharacteristics of the underlying protocol (e.g.,frequency offset). As theseconstraints can be a hindrance in real-world settings, we introduce apractical, generalizable approach that offers significant operational value fora variety of scenarios, including as an additional factor of authentication forpreventing impersonation attacks. Our goal is to identify artifacts intransmitted signals that are caused by a device's unique hardware"imperfections" without any knowledge about the nature of the signal. Wedevelop RF-DCN, a novel Deep Complex-valued Neural Network (DCN) that operateson raw RF signals and is completely agnostic of the underlying applications andprotocols. We present two DCN variations: (i) Convolutional DCN (CDCN) formodeling 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 spectrumwindow, without knowledge of the modulation scheme or even the carrierfrequencies. While our experiments demonstrate the effectiveness of our system,especially under challenging conditions where other neural networkarchitectures break down, we identify additional challenges in signal-basedfingerprinting and provide guidelines for future explorations. Our work laysthe foundation for more research within this vast and challenging space byestablishing fundamental directions for using raw RF I/Q data in novelcomplex-valued networks.

Deep Complex Networks for Protocol-Agnostic Radio Frequency Device Fingerprinting in the Wild