In order to improve the convergence speed, stability and state estimation accuracy of the traditional consensus Kalman filter algorithm, this paper proposes a consensus Kalman filter optimization algorithm based on fractional powers, that is, on the basis of the traditional consensus Kalman filter algorithm, fractional powers are introduced into the local Kalman filter part and the consensus fusion part respectively. The two better fractional power values are selected respectively and added to the traditional consensus Kalman filter algorithm at the same time. Through simulation experiments, it is validated that adjusting the fractional powers can notably expedite the convergence speed. Additionally, introducing fractional powers into the Kalman filtering process can also smooth error curves, enhancing stability and estimation accuracy. In comparison to introducing fractional powers separately in the Kalman filtering part and consensus fusion part, simultaneously introducing appropriate fractional powers in both parts demonstrates superior performance.
In this paper, the Inception of lightweight encrypted traffic classification with depthwise separable convolution is proposed. By fusing the depthwise separable convolution on the basis of Inception model, the model reduces the parameters and computation of the model to a large extent without reducing the accuracy. The main motivation of this paper is to reduce the consumption of the network as much as possible, and to develop a model with small computation and high precision. The effectiveness of the proposed method has been verified in the internationally open ISCX VPN-non VPN dataset test experiment. Firstly, the original data were cleaned and converted to transform the encrypted traffic samples into gray maps. Secondly, 1D-CNN and Inception model integrated with depthwise classifiable convolution were used for classification. The experimental results show that the classification accuracy rate, precision rate, recall rate and F1-score are 96.27%, 95.49%, 97.39% and 96.43% respectively for encrypted traffic classified by 12 categories.
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