Design of data-injection adversarial attacks against spatial field detectors

Roberto Lopez-Valcarce, Daniel Romero

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Data-injection attacks on spatial field detection corrupt a subset of measurements to cause erroneous decisions. We consider a centralized decision scheme exploiting spatial field smoothness to overcome lack of knowledge on system parameters such as noise variance. We obtain closed-form expressions for system performance and investigate strategies for an intruder injecting false data in a fraction of the sensors in order to reduce the probability of detection. The problem of determining the most vulnerable subset of sensors is also analyzed.

Original languageEnglish (US)
Title of host publication2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016
PublisherIEEE Computer Society
ISBN (Electronic)9781467378024
DOIs
StatePublished - Aug 24 2016
Event19th IEEE Statistical Signal Processing Workshop, SSP 2016 - Palma de Mallorca, Spain
Duration: Jun 25 2016Jun 29 2016

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings
Volume2016-August

Other

Other19th IEEE Statistical Signal Processing Workshop, SSP 2016
CountrySpain
CityPalma de Mallorca
Period6/25/166/29/16

Keywords

  • Adversarial detection
  • Byzantine sensors
  • cyber security
  • sensor networks
  • spatial field detection

Fingerprint Dive into the research topics of 'Design of data-injection adversarial attacks against spatial field detectors'. Together they form a unique fingerprint.

Cite this