TY - JOUR

T1 - Distributed Detection and Mitigation of Biasing Attacks over Multi-Agent Networks

AU - Doostmohammadian, Mohammadreza

AU - Zarrabi, Houman

AU - Rabiee, Hamid R.

AU - Khan, Usman A.

AU - Charalambous, Themistoklis

N1 - Publisher Copyright:
Author
Tallennetaan OA-artikkeli, kun julkaistu

PY - 2021/12

Y1 - 2021/12

N2 - 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.

AB - 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.

KW - Biasing Attacks

KW - Covariance matrices

KW - Distributed databases

KW - Distributed Estimation

KW - Distributed Observability

KW - Estimation

KW - False-Data Injection

KW - Noise measurement

KW - Observability

KW - Structural Analysis

KW - System dynamics

KW - Time measurement

UR - http://www.scopus.com/inward/record.url?scp=85115790568&partnerID=8YFLogxK

U2 - 10.1109/TNSE.2021.3115032

DO - 10.1109/TNSE.2021.3115032

M3 - Article

AN - SCOPUS:85115790568

VL - 8

SP - 3465

EP - 3477

JO - IEEE Transactions on Network Science and Engineering

JF - IEEE Transactions on Network Science and Engineering

SN - 2327-4697

IS - 4

ER -