ChebNet-LSTM-Based Secure Transmission Method in Multi-Cloud Networks

In the context of multi-cloud networks, enhancing transmission efficiency while maintaining security in communication relations is of paramount importance. Existing methods have been criticized for imposing high encryption overhead on the sender side and lacking effective short-term dynamic modeling of network performance. This paper proposes an optimization method that integrates a trusted obfuscation forwarding framework with the joint spatio-temporal network performance prediction. Firstly, to conceal communication relationships, we design a hop-by-hop encryption and proxy forwarding framework leveraging obfuscated forwarding nodes. Second, node selection is optimized by predicting network performance metrics. We extract the spatial correlations of the topology using a Chebyshev Network (ChebNet), and integrate this with Long Short-Term Memory (LSTM) models to capture temporal variations in performance. Experimental results show that the proposed method reduces the probability of communication relationship exposure to 3.57% from 100%, approaching the theoretical optimum. The prediction accuracies of bandwidth, delay, jitter and packet loss rate are significantly improved in terms of mean absolute error, root mean square error, and mean absolute percentage error metrics when compared to the graph convolutional network (GCN) and temporal convolutional network (TCN) benchmark models. Moreover, the node selection probability during obfuscated forwarding approaches a uniform distribution. The proposed method distributes the encryption load across intermediate nodes, thereby reducing the burden on the sender.