Cyber-security research on networked multi-sensor systems is crucial due to the vulnerability to various types of cyberattacks. For the development of effective defense measures, attention is required to gain insight ...
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Cyber-security research on networked multi-sensor systems is crucial due to the vulnerability to various types of cyberattacks. For the development of effective defense measures, attention is required to gain insight into the complex characteristics and behaviors of cyber attacks from the attacker's perspective. This paper aims to tackle the problem of distributed consensus estimation for networked multi-sensor systems subject to hybrid attacks and missing measurements. To account for both random denial of service (DoS) attacks and false data injection (FDI) attacks, a hybrid attack model on the estimator-to-estimator communication channel is presented. The characteristics of missing measurements are defined by random variables that satisfy the Bernoulli distribution. Then a modified consensus-based distributed estimator, integrated with the characteristics of hybrid attacks and missing measurements, is presented. For reducing the computational complexity of the optimal distributedestimation method, a scalable suboptimal distributedconsensus estimator is designed. Sufficient conditions are further provided for guaranteeing the stability of the proposed suboptimal distributed estimator. Finally, a simulation experiment on aircraft tracking is executed to validate the effectiveness and feasibility of the proposed algorithm.
In this paper, the problem of distributed consensus estimation with randomly missing measurements is investigated for a diffusion system over the sensor network. A random variable, the probability of which is known a ...
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In this paper, the problem of distributed consensus estimation with randomly missing measurements is investigated for a diffusion system over the sensor network. A random variable, the probability of which is known a priori, is used to model the randomly missing phenomena for each sensor. The aim of the addressed estimation problem is to design distributedconsensus estimators depending on the neighbouring information such that, for all random measurement missing, the estimation error systems are guaranteed to be globally asymptotically stable in the mean square. By using Lyapunov functional method and the stochastic analysis approach, the sufficient conditions are derived for the convergence of the estimation error systems. Finally, a numerical example is given to demonstrate the effectiveness of the proposed distributedconsensus estimator design scheme.
This article deals with the distributed extended object tracking with nonlinear noisy measurements. Therein, we use the orientation and semiaxes as individual parameters to model the spatial extent. To alleviate nonli...
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This article deals with the distributed extended object tracking with nonlinear noisy measurements. Therein, we use the orientation and semiaxes as individual parameters to model the spatial extent. To alleviate nonlinearity in the measurement function, we utilize the moment-matched approach in the linear minimum mean-square error sense to statistically linearize nonlinear measurements. In this setting, the required coefficients are computed by the sampling-based method. Taking the resulting measurements as a basis, two individual linear formulas with only additive noise are fed to the information filter (IF) framework. In a consensus-based IF that exchanges the local measurements with limited iterations, the estimates are inaccurate when the weighted priors are not incorporated. Motivated by this fact, we define an integrated information-weighted consensus rule including two steps, first toward the measurement-to-measurement and then toward the global weighted fusion on the priors. This leads us to propose a distributed generalized consensus on measurement (GCM) filter to achieve an agreement on both the kinematics and extent parameters. The estimation error of the GCM filter is proven to be exponentially bounded in the mean square. Results with two types of scenarios are presented with high estimation accuracy over alternative distributed approaches.
A false data injection attack in a wireless sensor network for cyber physical systems is designed. System state estimation is analyzed by remote distributedconsensus estimators, and the Kullback-Leibler divergence is...
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ISBN:
(纸本)9798350334722
A false data injection attack in a wireless sensor network for cyber physical systems is designed. System state estimation is analyzed by remote distributedconsensus estimators, and the Kullback-Leibler divergence is utilized as an indicator of the attack's stealthiness. The attack studied in this paper is injected into the wireless network channel between sensors and is Gaussian distributed with an arbitrary mean. Based on the relationship between the system performance under attack and stealthiness, a constrained optimization problem is given, and the optimal attack strategy can be calculated by solving the problem through the Lagrange multiplier method. Then based on theoretical research, the algorithm for generating the attack sequence is summarized. Finally, the theoretical analysis is justified through a numerical simulation example.
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