On Kalman Filtering with Compromised Sensors: Attack Stealthiness and Performance Bounds

Bai, Cheng-Zong; Gupta, Vijay; Pasqualetti, Fabio

doi:10.1109/tac.2017.2714903

Cited by 116 publications

(52 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Theorem 2. Considering the multisensor system (32) and (33) and the conditions A1 to A6 and the definitions (24) to (28), a centralized optimal filtering fusion algorithm is presented as follows.…”

Section: Centralized Optimal Filtering Fusion Algorithmmentioning

confidence: 99%

“…The multiplicative disturbance has stronger influence over sensors even than the attacks on cyberphysical systems (CPS). 23,24 Thus, when systems are polluted by multiplicative noise, one has to redesign filtering algorithm to filter the noises, otherwise, the filter will lose effectiveness. Optimal filters are presented in the sense of linear minimum mean square error (LMMSE) to settle the problem.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust fusion algorithms for multichannel stochastic multiplicative disturbance by multisensor observation

2020

Optim Control Appl Methods

View full text Add to dashboard Cite

Summary This article considers robust fusion algorithms for linear system with multichannel stochastic multiplicative disturbance by using projection theorem and projection formula. Based on the centralized and distributed (feedback and no feedback) fusion strategies, we present optimal filtering fusion algorithms, optimal fixed‐interval smoothing fusion algorithms, and fixed‐interval deconvolution fusion algorithms. The methods of projection theorem and projection formula are used to guarantee the optimality of these derived algorithms. The presented algorithms are much less computation burden and higher accuracy than existing state‐of‐the‐art methods. Numerical simulation results including a WSN network model verify the proposed algorithms.

show abstract

Section: Centralized Optimal Filtering Fusion Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust fusion algorithms for multichannel stochastic multiplicative disturbance by multisensor observation

2020

Optim Control Appl Methods

View full text Add to dashboard Cite

show abstract

“…To maximize the attack stealthiness, δ(k) is chosen as a zero-mean Gaussian sequence with covariance Σ [14]- [16]. The probability of attack-detection is proportional to the covariance of the attack sequence [14]- [16]. Therefore, Σ is limited to tr(Σ) ≤ η, where η captures the stealthiness of the attack.…”

Section: B Attack Modelmentioning

confidence: 99%

“…In contrast to (4) and (5), (13) and (14) capture the error dynamics in presence of a Byzantine attack.…”

Section: Joint Selection Of Byzantine Agents and Design Of Attacmentioning

confidence: 99%

“…For remote state estimation setting, optimal jamming policies for attacking the communication channels between sensors and fusion center to maximize the estimation error was proposed in [12] and optimal linear deception attack, which can successfully bypass a χ 2 false data detector, was presented in [13]. In [14] the mean square error (MSE) performance of single sensor Kalman filter with data-falsification attacks was analyzed considering the Kullback-Leibler (KL) divergence as a measure of attack stealthiness. Similarly, in [15] it was shown that with KL divergence as the stealth metric, the worst-case linear attack strategy that maximizes the estimation error covariance is a zero-mean Gaussian distributed attack sequence.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Coordinated Data-Falsification Attacks in Consensus-based Distributed Kalman Filtering

Moradi

Venkategowda

Werner

2019

2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

View full text Add to dashboard Cite

This paper considers consensus-based distributed Kalman filtering subject to data-falsification attack, where Byzantine agents share manipulated data with their neighboring agents. The attack is assumed to be coordinated among the Byzantine agents and follows a linear model. The goal of the Byzantine agents is to maximize the network-wide estimation error while evading false-data detectors at honest agents. To that end, we propose a joint selection of Byzantine agents and covariance matrices of attack sequences to maximize the network-wide estimation error subject to constraints on stealthiness and the number of Byzantine agents. The attack strategy is then obtained by employing block-coordinate descent method via Boolean relaxation and backward stepwise based subset selection method. Numerical results show the efficiency of the proposed attack strategy in comparison with other naive and uncoordinated attacks.

show abstract

Distributed Kalman filter for linear system with complex multi‐channel stochastic uncertain parameter and decoupled local filters

PuShi

YuXingkai

LiJianxun

2021

Adaptive Control & Signal

View full text Add to dashboard Cite

Summary This article considers a distributed Kalman filtering problem for linear system contaminated by complex multi‐channel random uncertain parameter in which a number of nodes cooperative without central coordination to estimate a common state based on local measurements and data received from neighbors. We propose an approach to eliminate this error propagation. The proposed local filters are guaranteed to be stable under some mild conditions on certain global structural data, and their fusion yields the centralized Kalman estimate. Then, we extend this method to smoothing and deconvolution algorithm. Finally, simulation experiments demonstrate the validity of the proposed approach.

show abstract

On Kalman Filtering with Compromised Sensors: Attack Stealthiness and Performance Bounds

Cited by 116 publications

References 15 publications

Robust fusion algorithms for multichannel stochastic multiplicative disturbance by multisensor observation

Robust fusion algorithms for multichannel stochastic multiplicative disturbance by multisensor observation

Coordinated Data-Falsification Attacks in Consensus-based Distributed Kalman Filtering

Distributed Kalman filter for linear system with complex multi‐channel stochastic uncertain parameter and decoupled local filters

Contact Info

Product

Resources

About