Network Intrusion Detection Method Based on Attention-BiTCN
In order to solve the problem of low accuracy of multi-classification in network intrusion detection field,the proposed algorithm analyzed the time series characteristics of network traffic data,an intrusion detection model based on attention mechanism and bi-directional temporal convolutional network(BiTCN)was convolutional neural network.In this model,the data set was pre-processed by heat-only coding and normalization to solve the problem of strong discreteness and different scale of network traffic data,and the pre-processed data were generated into bidirectional sequence by bidirectional sliding window method,attention-bitcn model was used to extract the bidirectional temporal features and integrate them in an additive manner to obtain the fusion features enhanced by temporal information.The proposed model is experimentally verified by the datasets of NSL-KDD and UNSW-NB15,and the accuracy of multiple classification reached 99.70％and 84.07％respectively,which is superior to traditional network intrusion detection algorithms and has more significant detection performance than other deep learning models.
NETL
NSTL
万方数据
维普
