… 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 …

… a network log-based CNN-LSTM hybrid prediction model for wireless network faults: firstly,
the network … are extracted by CNN, and finally, the extracted features are input into LSTM for …

… 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 …

… A hybrid model CNN-LSTM proposed in this paper efficiently captured all the spatial and …
that the CNN-LSTM performs better prediction compared to the CNN and LSTM models. …

… 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. …

… hybrid forecasting model that combines conventional neural network (CNN) with long short-term
memory network (LSTM… of a hybrid model on load forecasting over an individual LSTM …

… 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 …

… 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 …

… 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 …