Data Falsification Attacks on Consensus-Based Detection Systems

Bhavya Kailkhura, Swastik Brahma, Pramod K. Varshney

Research output: Contribution to journalArticlepeer-review

65 Scopus citations


This paper considers the problem of signal detection in distributed networks in the presence of data falsification (Byzantine) attacks. Detection approaches considered in the paper are based on fully distributed consensus algorithms, where all of the nodes exchange information only with their neighbors in the absence of a fusion center. For such networks, we first characterize the negative effect of Byzantines on the steady state and transient detection performance of conventional consensus-based detection algorithms. To avoid performance deterioration, we propose a distributed weighted average consensus algorithm that is robust to Byzantine attacks. We show that, under reasonable assumptions, the global test statistic for detection can be computed locally at each node using our proposed consensus algorithm. We exploit the statistical distribution of the nodes' data to devise techniques for mitigating the influence of data falsifying Byzantines on the distributed detection system. Since some parameters of the statistical distribution of the nodes' data might not be known a priori, we propose learning based techniques to enable an adaptive design of the local fusion or update rules. Numerical results are presented for illustration.

Original languageEnglish (US)
Pages (from-to)145-158
Number of pages14
JournalIEEE Transactions on Signal and Information Processing over Networks
Issue number1
StatePublished - Mar 2017
Externally publishedYes


  • Ad-hoc cognitive radio networks
  • Byzantines
  • consensus algorithms
  • data falsification attacks
  • distributed detection
  • spectrum sensing

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Computer Networks and Communications


Dive into the research topics of 'Data Falsification Attacks on Consensus-Based Detection Systems'. Together they form a unique fingerprint.

Cite this