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- W4387423258 abstract "This paper presents a K-means and gaussian mixture model (GMM) based detection and fusion algorithm for a multisensory cyber physical system (CPS). In the considered system, part of measurement channels may suffer from false data injection (FDI) attacks, which would deteriorate the estimation performance of the CPS. To handle this, a novel detection and fusion algorithm is proposed to eliminate compromised sensors and fuse safe sensors. Firstly, the K-means algorithm is utilized to get rid of severely biased sensors and the GMM algorithm is subsequently adopted to further detect sensors screened by the K-means algorithm. Moreover, a more computationally efficient sequential Kalman filter is used at the remote estimator side, and the detection and fusion algorithm based on K-means and GMM algorithms is derived in the framework of the sequential Kalman filter. In addition, the recursion of the estimation error covariance is recalculated in the presents of attacks. Finally, the effectiveness of the detection and fusion algorithm is verified by a simulation example of an unmanned ground vehicle (UVA)." @default.
- W4387423258 created "2023-10-08" @default.
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- W4387423258 date "2023-01-01" @default.
- W4387423258 modified "2023-10-18" @default.
- W4387423258 title "A K-Means and GMM-Based Fusion and Detection Algorithm Against FDI Attacks on Remote Estimator" @default.
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- W4387423258 doi "https://doi.org/10.1007/978-981-99-6882-4_12" @default.
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