… In this study, we adopted a malicious traffic analysis method based on CNN and LSTM to
extract and analyze network traffic information of network raw dataset from both spatial and …

… In our study, we employed the Convolutional Neural Network 603 (CNN) and Long Short-Term
Memory (LSTM) algorithms 604 (LSTM). Using three layers of hybrid CNN and LSTM, the …

… hybrid network of convolutional neural network (CNN) and long short-term memory (LSTM)
network … of passing input features through the deep network to increase feature diversity. The …

… In this work, we have introduced two more LSTM layers, … , the present paper investigates a
hybrid DL architecture for … Neural Network and Long Short-term Memory (CNN-LSTM) model. …

… stability, for forecast d CNN-LSTM 5 concentration t alternative riate LSTM riate CNN-LST
are compared ed to evaluate (MAE) and roo e reminder of n 2, the meth LSTM are pre M model …

… a hybrid CNN-LSTM model, which can exploit the benefits of convolutional layers and LSTM
layers; (… The proposed CNN-LSTM model is presented in Section IV. Finally, the results and …

… a nucleotide-level hybrid deep learning method based on a CNN and LSTM network together.
… The results indicate that the hybrid CNN and LSTM networks can be employed to achieve …

… of network parameters, we find that increasing the number of filters as well as LSTM units
(width) is the most effective way to improve the performance of the 1D-CNN-LSTM model. But it …

… In this paper, we propose a CNN-LSTM hybrid network that can … CNN-LSTM hybrid networks,
which linearly combine convolutional neural network (CNN), long short-term memory (LSTM…

… In this article, we proposed a hybrid model by integrating the CNN with LSTM algorithms in
order to improve the SDN capability to detect the malicious network activities. One of the main …