Rewrite in academic way with a convince style for an article in the proposed method section: The classifier architecture is a multi-layered model designed to extract features from raw data and classify network flows based on historical data. The model begins with CNN layers, which are used to extract high-level features from the raw data. These features are then fed into LSTM layers, specialized for temporal data, to capture sequential patterns over time. Next, the attention mechanism is applied to identify the most important features for classification. This improves the accuracy and efficiency of the model by focusing on the most relevant information. The fully connected layers with SoftMax activation function are then used to classify the following flow based on historical data. In Figure 6, the architecture of the model is shown, illustrating the different layers and their connections. Within the attention layer, three types of attention are compared to determine the best model for classification in terms of f1-score and time detection. These attention mechanisms include additive attention, self-attention, and multi-head attention, where multi-head attention performs attention operations in parallel. Furthermore, the transformer duo, a popular deep learning model for natural language processing, is also utilized to improve the accuracy of the time series classification model. To reduce the time detection of the model, an autoencoder is employed to compress and reduce the volume of data, as seen in Figure 11