Simulation of Cloud Computing Network Security Intrusion Detection Model Based on Neural Network Algorithm Driven by Big Data

In the big data cloud computing environment, because of the openness and self-organization of information interaction, the cloud computing network is vulnerable to attacks. This article studies the cloud computing network security intrusion detection model driven by big data to improve the network security defense ability. In most applications, it is based on back propagation neural network (BPNN) or its variant. In this article, the security intrusion detection model of cloud computing network based on optimized BPNN is established, and different learning functions are selected and momentum learning factors are added to improve it. The simulation results show that the model can effectively improve the detection performance of cloud computing network intrusion, and has a high ability to identify attack characteristics. The optimization strategy in this article can greatly reduce the dimension of neural network input data without losing the original data information, improve the real-time performance of the system and simplify the structure of neural network.