Inference Attacks on Physical Layer Channel State Information

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributedpeer-review

Contributors

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

Original languageEnglish
Title of host publication2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)
PublisherIEEE TechRxiv
Pages935-942
Number of pages8
ISBN (Print)978-1-6654-0393-1
Publication statusPublished - 29 Dec 2021
Peer-reviewedYes

Conference

Title2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)
Duration29 December 2021 - 1 January 2022
LocationGuangzhou, China

External IDs

Scopus 85101202170

Keywords

Keywords

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