Network Intrusion Detection Method Integrating CNN and BiLSTM
摘要
To address the problem of low network intrusion detection accuracy and high false positive rate,a network intrusion detection method integrating Convolutional Neural Network(CNN) and Bidirectional Long Short-Term Memory(BiLSTM) network is proposed.The KDDcup99 data set is preprocessed,and the local features and long-distance dependent features are extracted using the CNN model and the BiLSTM model,respectively.The importance of features is calculated by using the introduced attention mechanism,and the final classification results are obtained by using the softmax classifier.Experimental results show that compared with CNN-based and LSTM-based methods,the proposed method has better network intrusion detection performance,its accuracy can be improved to 95.0%,and the false detection rate can be reduced to 5.1%.