## 2 Literature Review
Various methods, including SVM, LSTM, and CNN, are compared with the proposed model. In terms of minimum values for MSE and MAE, the proposed model, which is called FDG-trans, outperforms all other models. To detect the distributed denial of service attack (DDoS), [PERSON]. [44] proposed an effective and adaptive intrusion detection system by using LSTM and convolution neural network (CNN). Based on the CIC-DDoS2019 dataset, a proposal has been developed for detecting different types of DDoS attacks. A CICFlowMeter-V3 network was used to develop the dataset. Several performance measures were used to evaluate the model, including precision, recall, F1-score, and accuracy. It was found that the proposed CNN-LSTM model was capable of reaching a high degree of precision (100%) concerning all the evaluation metrics. To predict noisy intraday stock prices, [PERSON]. [45] proposed a hybrid model combining CEEMD, entropy, GRU, and history attention (CEGH). Four steps make up the implementation of the suggested model. In the first step, the complex stock price data is decomposed into different components, technically known as IMFs, with the help of complete ensemble empirical mode decomposition (CEEMD). Then, for each IMF, the sample entropy (SampEn) values and approximation entropy (ApEn) values are used to remove noise. The remaining IMFs were then aggregated into four groups, and a feedforward neural network (FNN) or recurrent gate unit with historical attention (GRU-HA) was used to forecast the comprehensive signals. Integrating the outcomes of each group's predictions yields the final forecast. For checking the prediction performance of the suggested model, two stock markets such as China and U.S., are used as a real-world scenario. It is evident from the empirical results that the proposed CEGH model outperforms the other models in terms of prediction accuracy. A hybrid model that combines the features of a Graph Convolutional Network (GCN) and a Bidirectional Long Short-Term Memory (BiLSTM) network was suggested by [46] to predict crude oil prices. It provides new possibilities for the analysis of time series and improves existing results used in previous studies. Based on the minimum values of RMSE, MSE, MAPE, and the R-squared (R\({}^{2}\)), the prediction accuracy of the proposed BiLSTM-GCN is better than the BiLSTM, GCN, and the orthodox models. A slope-based method (ISBM) is based on empirical mode decomposition (EMD) and feed-forward neural networks (FNN), namely, the EMD-ISBM-FNN method proposedby [47] to decompose and forecast crude oil prices. The complex nonlinear and nonstationary Brent crude oil data are divided into several IMFs and single montone residues in the first step of the EMD approach. In order to train the architect of the FNN model, these subcomponents are then used as input features. Compared to the single FNN and EMD-FNN, the performance of the suggested model, known as the EMD-ISBM-FNN, is higher. The available literature is the motivation factor behind implementing a hybrid Akim-EMD-LSTM model to forecast the stock market index. Since it is difficult to predict the nonlinear and nonstationary patterns of the stock market with the help of the classical time series and other models used in the previous literature. Hence, consideration is given to EMD and its different types, including the proposed new version of breaking down the complex stock prices into simple subcomponents that can be modeled easily with the help of the LSTM network. In this research article, we propose a new version of EMD that is based on the Akima spline interpolation technique and then integrate this new method with the LSTM network that uses the correlated subcomponents (residue) with the actual data. Detail description of our proposed model is given in Section 3.5.