## 4 Proposed Model Structure  
Our approach uses autoencoders, a method that is now popular in deep learning. An autoencoder is an unsupervised neural network-based feature extraction method that learns the best feasible factors to reproduce faithfully its output given some input. One of its many appealing features is its potential to provide a non-linear and more efficient generalization than the principal component analysis (PCA). It achieves this result by backpropagation with input-equivalent target values. To rephrase, it tries to figure out how to predict the occurrence of itself as closely as possible. The typical architecture of an autoencoder consists of three layers: an input layer, an output layer, and a hidden layer. The hidden layer's dimensions are lower than that of the input [24]. Figure 1 shows the traditional (single) autoencoders. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref** & **Model** & **Achievement** \\ \hline [PERSON], [PERSON] [12] & LSTM, GRU, CNN-LSTM, and 3-LSTM & 3-LSTM outperformed the other models which gained \\  & combined the stacked autoencoder with a & 99.450\% in accuracy \\  & random forest classifier & They asserted that the soft-max layer was less effective \\  & & random forest classifier & than traditional classifiers \\  & & For the ISCX2012 dataset, the model achieved 0.9669 in precision, 0.9649 in recall, 0.9657 in F1-score, and 0.9666 \\  & & in accuracy. For C1C-C12071, the model achieved 0.9797 in precision, 0.9765 in recall, 0.9780 in F1-score, and 0.9772 in accuracy \\  & & The model outperformed the other ML models, in which it gained 99.4\% in accuracy compared with RNN LSTM \\  & & that archived 89.63\%, SVM achieved 86.85\%, and Naive \\  & & Bayes achieved 82.61\% \\  & & The accuracy score was 93.76\% and the recall \\  & & score was 93.26\% and the recall \\  & & The model achieved good results when using CICDDoS \\  & & dense neural network (DNN), and a gated \\  & & recurrent unit (GRU)) & The model achieved good results when using CICDDoS \\  & & dense neural network (DNN), and a gated \\  & & recurrent unit (GRU)) & The model achieved good results when using CICDDoS \\  & & dense neural network (DNN), and a gated \\  & & recurrent unit (GRU)) & The model achieved good results  
In the proposed method, we utilize a deep autoencoder. Unlike traditional autoencoders, deep autoencoders consist of two typical deep-belief networks, one for encoding and one for decoding, with four or five shallow layers each. Deep learning can be applied to autoencoders by a stacked autoencoder, in which many hidden layers build depth, and the hidden layers reflect fundamental concepts. As a result of this increased depth, computing costs will reduce, the amount of instruction data required will decrease, and accuracy will improve. The output of one buried layer serves as the input to a later, more advanced step. First-order features are often learned from unprocessed data by the first layer of a stacked autoencoder. Second-order features based on trends in the presence of first-order traits are typically learned by the second layer. Subsequent layers build our understanding of higher-order characteristics. Figure 2 shows the structure of the proposed deep autoencoder model. The first layer is the input layer, which receives input Xi and uses numerous hidden layers to encode and decode it (encoder and decoder blocks). The encoding process compresses the attributes to make them smaller than the input data, and the decoding process restores these attributes in reverse order to begin the final output at the deepest layer. When processed, the output feature vector Xi is virtually identical to the input. The convolutional layer and LSTM are combined with an autoencoder to generate a robust DDoS attack classifier. LSTM is excellent at understanding the context of Internet packets, identifying long- and short-term dependencies, and identifying trends in DDoS attack sequences.