Intrusion Detection Based on Convolutional Neural Network

To solve the problem of low accuracy and low adaptability of traditional intrusion detection technology, we propose an intrusion detection algorithm based on convolutional neural network. In this paper, two convolution layers and pooling layers are used, and a batch normalization layer is added after each convolution layer to improve the speed of network and avoid mode collapse. During the experiment, SGD and Adam optimizers were used to train the model respectively. The results show that Adam optimizer has better performance. When epoch =200, the model precision average value can reach 0.9507, F1 average value can reach 0.9438.