A NETWORK INTRUSION DETEDTION METHOD BASED ON CONVOLUTIONAL NEURAL NETWORK AND FEATURE FUSION

In order to solve the problems of few attack features, data imbalance and slow convergence in traditional network intrusion detection methods, this paper proposes an intrusion detection method based on convolutional neural network and feature fusion. This method converted the traffic data into a gray image to extract its texture features, and fused the texture features with network traffic features to increase the amount of attack characteristics. The Borderline-SMOTE method was used to balance the UNSW-NB15 data set. The greedy layer-wise training method was used to optimize the convolutional neural network model to improve the convergence speed of the model. Experiments show that the performance of this method is better than other detection methods, and the accuracy rate can be increased to 96.38%.