Inference Attacks on Physical Layer Channel State Information

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Beitragende

Abstract

In Physical Layer Security, knowing the reciprocal state information of the legitimate terminals' wireless channel is considered a shared secret. Although questioned in recent works, the basic assumption is that an eavesdropper, residing more than half of a wavelength away from the legitimate terminals, is unable to even obtain estimates that are correlated to the state information of the legitimate channel. In this work, we present a Machine Learning based attack that does not require knowledge about the environment or terminal positions, but is solely based on the eavesdropper's measurements. It still successfully infers the legitimate channel state information as represented in impulse responses. We show the effectiveness of our attack by evaluating it on two sets of real world ultra wideband channel impulse responses, for which our attack predictions can achieve higher correlations than even the measurements at the legitimate channel.

Details

OriginalspracheEnglisch
Titel2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)
Herausgeber (Verlag)IEEE TechRxiv
Seiten935-942
Seitenumfang8
ISBN (elektronisch)9781665403924
ISBN (Print)978-1-6654-0393-1
PublikationsstatusVeröffentlicht - 29 Dez. 2021
Peer-Review-StatusJa

Konferenz

Titel2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications
KurztitelTrustCom 2020
Veranstaltungsnummer19
Dauer29 Dezember 2020 - 1 Januar 2021
StadtGuangzhou
LandChina

Externe IDs

Scopus 85101202170

Schlagworte

Schlagwörter

  • Channel state information, Position measurement, Privacy, Security, Ultra wideband technology, Wavelength measurement, Wireless communication