  In recent years, deep learning poses a deep technical revolution in almost
every field and attracts great attentions from industry and academia.
Especially, the convolutional neural network (CNN), one representative model of
deep learning, achieves great successes in computer vision and natural language
processing. However, simply or blindly applying CNN to the other fields results
in lower training effects or makes it quite difficult to adjust the model
parameters. In this poster, we propose a general methodology named V-CNN by
introducing data visualizing for CNN. V-CNN introduces a data visualization
model prior to CNN modeling to make sure the data after processing is fit for
the features of images as well as CNN modeling. We apply V-CNN to the network
intrusion detection problem based on a famous practical dataset: AWID.
Simulation results confirm V-CNN significantly outperforms other studies and
the recall rate of each invasion category is more than 99.8%.
