Abstract: Aiming at the problem of low network intrusion detection performance, a deep learning intrusion detection model CTA-net based on space-time feature fusion and attention mechanism is proposed. The model obtains space-time fusion features by integrating convolutional neural network (CNN) and long-short-term memory network (LSTM), and then uses the attention module (Attention) to calculate the importance of the input space-time fusion features, and finally passes the softmax function sort. Using the NSL-KDD data set, the experimental results show that compared with the CNN model with similar structure and the space-time fusion CNN-LSTM model, the convergence of the training set is significantly improved, and the accurate of classification evaluation index used on the test set  has increased by 10.9120 percentage points and 11.8740 percentage points, the precision has increased by 9.1950 percentage points  and 9.6130 percentage points, the recall has increased by 9.1780 percentage points  and9.9340 percentage points, and F1-SCORE has increased by 10.7830 percentage points  and 11.750 percentage points . The simulation results show that the proposed CTA-net model has good application potential in network intrusion detection.

Key words: network intrusion detection, convolution neural network, long-short-term memory network, attention mechanism, NSL-KDD