Study on Network Security Based on PCA and BP Neural Network Under Green Communication

In response to the energy consumption caused by the exponential growth of mobile network capacity demand, the Chen Shanzhi team proposed a user-centric ultra-dense network. It dynamically organizes multiple access nodes into a group of access nodes centered on the user. It can “accompany” user services without perception. The result is increased system capacity and user experience. This paper systematically analyzes the key security issues of user-centric ultra-dense networks. Based on the security characteristics of the access node group, a security system adapted to the user-centered ultra-dense network architecture is designed. Aiming at the problem of data security transmission between network entities, a lightweight data security transmission algorithm based on implicit certificate is proposed. The algorithm uses a lightweight implicit certificate to generate a temporary session key by means of a reconfigurable public-private key pair, so as to encrypt and protect the transmitted data, and solve the problem of data security transmission when the access nodes cooperate. The simulation shows that the algorithm consumes 34kB less on average than the traditional key method in different scenarios, and the speed is fast and the encryption key remains stable. At the same time, it also saves storage space and can be conveniently applied to access nodes with limited resources. Then, aiming at the network information security problem, an information security detection method based on improved BP neural network based on PCA is proposed. Simulation experiments were carried out on four types of attack types using the KDD CUP data set. The simulation results show that the improved BP neural network classifier combined with PCA has higher performance in network training. The false alarm rate for the DOS, R2L and PROBE attack types dropped to about 10%, and the false alarm rate for the U2R attack types dropped by 8.07%.