Network intrusion detection based on improved CNN-BiLSTM and three-way decision
Aiming at the problems of insufficient feature extraction of network intrusion detection model and imbalance of re-lated dataset,a network intrusion detection method based on convolutional neural network and bidirectional long short-term memory network is proposed.Firstly,the UNSW-NB15 dataset is preprocessed by combining the SMOTE oversampling algorithm.Secondly,the intrusion detection model based on CNN-BiLSTM is established to extract the local features and long-distance-dependent features of the dataset,and the importance of the features is further strengthened by the attention mechanism.Finally,the classifica-tion results are obtained by the classifier based on the three-branch decision.The experimental results show that the proposed method is improved in each evaluation index and can effectively improve the detection performance.
intrusion detectionconvolutional neural networksbidirectional long short-term memory networksthree-way decision
NETL
NSTL
万方数据
