Distributed Detection and Mitigation of Biasing Attacks over Multi-Agent Networks

Mohammadreza Doostmohammadian, Houman Zarrabi, Hamid R. Rabiee, Usman A. Khan, Themistoklis Charalambous

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
25 Downloads (Pure)


This paper proposes a distributed attack detection and mitigation technique based on distributed estimation over a multi-agent network, where the agents take partial system measurements susceptible to (possible) biasing attacks. In particular, we assume that the system is not locally observable via the measurements in the direct neighborhood of any agent. First, for performance analysis in the attack-free case, we show that the proposed distributed estimation is unbiased with bounded mean-square deviation in steady-state. Then, we propose a residual-based strategy to locally detect possible attacks at agents. In contrast to the deterministic thresholds in the literature assuming an upper bound on the noise support, we define the thresholds on the residuals in a probabilistic sense. After detecting and isolating the attacked agent, a system-digraph-based mitigation strategy is proposed to replace the attacked measurement with a new observationally-equivalent one to recover potential observability loss. We adopt a graph-theoretic method to classify the agents based on their measurements, to distinguish between the agents recovering the system rank-deficiency and the ones recovering output-connectivity of the system digraph. The attack detection/mitigation strategy is specifically described for each type, which is of polynomial-order complexity for large-scale applications. Illustrative simulations support our theoretical results.

Original languageEnglish
Pages (from-to)3465-3477
Number of pages13
JournalIEEE Transactions on Network Science and Engineering
Issue number4
Early online date24 Sep 2021
Publication statusPublished - Dec 2021
MoE publication typeA1 Journal article-refereed


  • Biasing Attacks
  • Covariance matrices
  • Distributed databases
  • Distributed Estimation
  • Distributed Observability
  • Estimation
  • False-Data Injection
  • Noise measurement
  • Observability
  • Structural Analysis
  • System dynamics
  • Time measurement


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