Attack detectors for data aggregation in clustered sensor networks

Roberto Lopez-Valcarce, Daniel Romero

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

2 Scopus citations

Abstract

Among many security threats to sensor networks, compromised sensing is particularly challenging due to the fact that it cannot be addressed by standard authentication approaches. We consider a clustered scenario for data aggregation in which an attacker injects a disturbance in sensor readings. Casting the problem in an estimation framework, we systematically apply the Generalized Likelihood Ratio approach to derive attack detectors. The analysis under different attacks reveals that detectors based on similarity of means across clusters are suboptimal, with Bartlett's test for homoscedasticity constituting a good candidate when lacking a priori knowledge of the variance of the underlying distribution.

Original languageEnglish (US)
Title of host publication2015 23rd European Signal Processing Conference, EUSIPCO 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2053-2057
Number of pages5
ISBN (Electronic)9780992862633
DOIs
StatePublished - Dec 22 2015
Event23rd European Signal Processing Conference, EUSIPCO 2015 - Nice, France
Duration: Aug 31 2015Sep 4 2015

Publication series

Name2015 23rd European Signal Processing Conference, EUSIPCO 2015

Other

Other23rd European Signal Processing Conference, EUSIPCO 2015
CountryFrance
CityNice
Period8/31/159/4/15

Keywords

  • attack detection
  • resilient data aggregation
  • sensor networks

Fingerprint Dive into the research topics of 'Attack detectors for data aggregation in clustered sensor networks'. Together they form a unique fingerprint.

Cite this