Botnet detection method based on CNN-LSTM fusion

The invention discloses a botnet detection method based on CNN-LSTM fusion, and the method comprises the steps: obtaining a network data set, and carrying out the preprocessing of the data set; constructing a detection model, wherein the detection model comprises a convolutional neural network model CNN, a long and short term memory network model LSTM, a feature fusion module and a full connection layer, the convolutional neural network model CNN is used for spatial feature extraction, and the long and short term memory network model LSTM is used for time sequence feature extraction; performing feature fusion on the extracted spatial features and time sequence features in a feature fusion module to obtain fused features, and outputting a detection result through a full connection layer by the fused features; and training the detection model to obtain a trained detection model. According to the method, operations such as manual feature extraction are simplified, extremely strong priori knowledge is not needed,