Two novel schemes are proposed to teleport an unknown two-level quantum state probabilistically when the sender and the receiver only have partial information about the quantum channel, respectively. This is distinct from the fact that either the sender or the receiver has entire information about the quantum channel in previous schemes for probabilistic teleportation. Theoretical analysis proves that these schemes are straightforward, efficient and cost-saving. The concrete realization procedures of our schemes are presented in detail, and the result shows that our proposals could extend the application range of probabilistic teleportation.

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Existing SYN flood attack detection methods have obvious problems such as poor feature selectivity, weak generalization ability, easy overfitting, and low accuracy during training. In the paper, we present a SYN flood attack detection method based on the Hierarchical Multihad Self-Attention (HMHSA) mechanism. First, we use one-hot encoding and normalization to preprocess traffic data. Then the preprocessed traffic data is transmitted to the Feature-based Multihead Self-Attention (FBMHA) layer for feature selection. Finally, we use data slices to determine the features of the preprocessed traffic data under time series by passing the preprocessed traffic data into the Slice-based Multihead Self-Attention (SBMHA) layer. We tested the proposed method on different datasets. The experimental results show that compared with other works, our method presents better in feature selection and higher detection accuracy (even up to 99.97%).

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