Research on Intelligent Monitoring System of Urban Network Database

City network database is vulnerable to attack. Smart city IOT (Internet of Things) needs to implement intelligent monitoring systems to protect network systems. The traditional convolutional neural network needs more advantages in dealing with real-time network intrusion. This paper proposes a new detection method using a visualization method when real-time traffic data is missing. We built a real-time traffic detection platform and invented a new algorithm structure: The system uses offline data to digitize and pixelate the hacked network traffic and then carries out gray-scale mapping, a pre-processing step. The data is rotated to the right and connected to the CNN interface for training. We use real-time network data to verify the experiment and evaluate our proposed model. The results show that the scheme proposed in this paper has a good detection effect and can detect real-time network intrusion types.