Jul 12, 2023 · This paper proposes a network encryption traffic classification model that combines attention mechanisms and spatiotemporal features.

This paper proposes a network encrypted traffic classification model which combines attention mechanism with spatial and temporal characteristics.

Incorporating a channel attention mechanism serves to avoid irrelevant noise, thereby effectively integrating spatio-temporal features. This approach provides ...

The experimental results show that the model can be applied to classify network traffic of encrypted and unencrypted applications at the same time, ...

Abstract: Traffic classification is widely used in network security and network management. Early studies have mainly focused on mapping network traffic to ...

Jul 1, 2023 · The model firstly uses the long short-term memory (LSTM) method to analyze continuous network flows and find the temporal correlation features ...

This paper proposes a network encryption traffic classification model that combines attention mechanisms and spatiotemporal features.

Nov 21, 2023 · This paper proposes a network encryption traffic classification model that combines attention mechanisms and spatiotemporal features.

Apr 1, 2021 · The model first learns the temporal characteristics on the data set, Then, combined with the attention mechanism, which the key feature ...

Apr 1, 2021 · The model first uses LSTM (Long ShortTerm Memory) to analyze the time series of the continuous network flows and find out the time ...