Distributed Kalman filtering for robust state estimation over wireless sensor networks under malicious cyber attacks

Wen, Fuxi; Wang, Zhongmin

doi:10.1016/j.dsp.2018.03.002

“…Remark 2. Note that noise Gaussianity is a standard assumption in most distributed estimation/filtering and attack detection literature, e.g., see [2]- [6], [8], [17]- [20], [28], [30], [31], [43], [44], [47], [48], [60], [63], [67], [69]- [71], [85]- [87].…”

Section: Linear Dynamical Systemmentioning

confidence: 99%

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

Doostmohammadian

¹

,

Zarrabi

²

,

Rabiee

³

et al. 2021

IEEE Trans. Netw. Sci. Eng.

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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.

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“…The performance of proposed trust-based unscented Kalman filtering based on modified secure node strategy is evaluated for the problem of manoeuvring target tracking under different types of cyber-attacks via computer simulations in this section. Besides, the results are compared with the related meth-ALGORITHM 1 Distributed trust-based unscented Kalman filter ods which have used a majority voting strategy [3], secure node strategy [22], and also standard data fusion. Forasmuch as a rotational manoeuvre is considered in the target tracking problem, the moving target model is highly non-linear.…”

Section: Simulationsmentioning

confidence: 99%

Distributed trust‐based unscented Kalman filter for non‐linear state estimation under cyber‐attacks: The application of manoeuvring target tracking over wireless sensor networks

Adeli

¹

,

Hajatipour

²

,

Yazdanpanah

³

et al. 2021

IET Control Theory & Appl

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This paper is concerned with secure state estimation of non-linear systems under malicious cyber-attacks. The application of target tracking over a wireless sensor network is investigated. The existence of rotational manoeuvre in the target movement introduces non-linear behaviour in the dynamic model of the system. Moreover, in wireless sensor networks under cyber-attacks, erroneous information is spread in the whole network by imperilling some nodes and consequently their neighbours. Thus, they can deteriorate the performance of tracking. Despite the development of target tracking techniques in wireless sensor networks, the problem of rotational manoeuvring target tracking under cyberattacks is still challenging. To deal with the model non-linearity due to target rotational manoeuvres, an unscented Kalman filter is employed to estimate the target state variables consisting of the position and velocity. A diffusion-based distributed unscented Kalman filtering combined with a trust-based scheme is applied to ensure robustness against the cyber-attacks in manoeuvring target tracking applications over a wireless sensor network with secured nodes. Simulation results demonstrate the effectiveness of the proposed strategy in terms of tracking accuracy, while random attacks, false data injection attacks, and replay attacks are considered.Recently, cyber-physical systems (CPSs) have received widespread attention in different fields of studies, such as industrial automation systems, transportation networks, smart grids, and wireless sensor networks (WSNs) [1,2]. WSNs have a wide range of applications, among which, target tracking is one of the most practical applications. Other applications include environmental monitoring, information collection, and control of unmanned aerial vehicles [3,4]. A typical distributed WSN consists of several sensors that communicate with the rest of the network. In a distributed WSN, a sensor node collaborates with its neighbouring sensors to estimate the states of the target based on a given graph topology. Thus, the problem of target tracking over a WSN is considered as a distributedThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Remark 2. Note that noise Gaussianity is a standard assumption in most distributed estimation/filtering and attack detection literature, e.g., see [2]- [6], [8], [17]- [20], [28], [30], [31], [43], [44], [47], [48], [60], [63], [67], [69]- [71], [85]- [87].…”

Section: Linear Dynamical Systemmentioning

confidence: 99%

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

Doostmohammadian¹,

Zarrabi²,

Rabiee³

et al. 2021

Preprint

0

View full text Add to dashboard Cite

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.

show abstract

Distributed Kalman filtering for robust state estimation over wireless sensor networks under malicious cyber attacks

Cited by 33 publications

References 14 publications

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

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

Distributed trust‐based unscented Kalman filter for non‐linear state estimation under cyber‐attacks: The application of manoeuvring target tracking over wireless sensor networks

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

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