Very often, a measurement is transmitted through network systems before it is available for filtering. The network systems, designed with several communication channels, are prone to cyber-attacks. The cyber-attack often injects false data to alter the original measurement. This article develops a modified Bayesian approximation filtering method for nonlinear filtering with measurements altered due to cyber-attack. The proposed development is within the scope of nonlinear Gaussian filtering. It considers the false data to have either additive or multiplicative effect over the original measurement. Subsequently, two modified measurement models are introduced to model the possibility of false data stochastically. Then, the traditional nonlinear Gaussian filtering method is redesigned for the modified measurement models to deal with the false data attack. The proposed modification is applicable to any of the existing nonlinear Gaussian filters, such as extended Kalman filter, unscented Kalman filter, cubature Kalman filter, and Gauss–Hermite filter. The simulation results show an enhanced estimation accuracy for the proposed modification over the traditional nonlinear Gaussian filtering in the presence of false data.
Bayesian Approximation Filtering With False Data Attack on Network
IEEE Transactions on Aerospace and Electronic Systems ; 58 , 2 ; 976-988
2022-04-01
3091757 byte
Article (Journal)
Electronic Resource
English
Incremental Density Approximation and Kernel-Based Bayesian Filtering for Object Tracking
British Library Conference Proceedings | 2004
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