基于神经网络的访问控制策略优化模型

访问控制是网络安全防范和保护的主要核心策略，其主要任务是保证网络资源不被非法使用和访问。将风险概念引入访问控制，分析了基于风险的权限委托以及权限再分配的基本性质;基于MUS集合的计算方法，给出了一种基于神经网络的风险评估方法。针对神经网络适合定量数据，而风险因素的指标值具有很大的不易确定性等问题，采用模糊评价法量化信息安全的风险因素指标，对神经网络的输入进行模糊预处理。仿真结果表明，模糊神经网络经过训练，可以实时地佑算风险因素的级别。

Access Control Policy Optimization Model Based on Neural Network

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.