Access Control Policy Optimization Model Based on Neural Network
基于神经网络的访问控制策略优化模型

Access control is the main core policy of network security and protection, and its main task is to ensure that network resources arc not illegal use and access. Pulling the concept of the risk into access control, analysed delegate permissions based on risk and the basic nature of permissions of redistribution, and the calculation based on MUS collection, proposed a risk assessment based on neural network. Since the neural network is suited for the quantity data processing, and the risk factors arc of great uncertainty, the risk factors of information security were quantized by fuzzy evaluation method and the input of neural network was pretreatmented. The simulation results show that the trained neural network can estimate the degree of risk factor real time.