期刊
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
卷 58, 期 2, 页码 976-988出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAES.2021.3117664
关键词
Biomedical measurement; Stochastic processes; Data models; Bayes methods; Additives; Noise measurement; Kalman filters; Cyber-attack; false data; Gaussian filtering; nonlinear filtering
资金
- Department of Science and Technology (DST), Government of India under the INSPIRE Faculty Award
This article presents a modified Bayesian approximation filtering method to deal with measurements altered due to cyber-attacks. By introducing modified measurement models and redesigning the traditional nonlinear Gaussian filtering method, the proposed method is able to effectively handle the presence of false data and improve estimation accuracy.
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.
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