DDoS attacks occur frequently. This paper proposes a DDoS attack detection method that integrates self attention mechanism and CNN-BiLSTM to address the problem of low accuracy and high false positive rate in classification tasks due to the high and multiple feature dimensions of raw traffic data. Firstly, the random forest algorithm is combined with Pearson correlation analysis to select important features as model inputs to reduce the redundancy of input data. Secondly, one-dimensional convolutional neural networks and bidirectional long-term and short-term memory networks are used to extrac...
