基于神经网络的访问控制策略优化模型  

Access Control Policy Optimization Model Based on Neural Network

作　　者：李肯立[1] 康强[1] 唐卓[1] 沙行勉[1] 杨柳[1] 

机构地区：[1]湖南大学计算机与通信学院,长沙410082

出　　处：《计算机科学》2011年第11期87-91,共5页Computer Science

基　　金：国家自然科学基金项目(90715029;61070057);中国博士后科学基金(20060400845);中国博士后科学基金面上项目(20100480936)资助

摘　　要：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,可以实时地估算风险因素的级别。Access control is the main core policy of network security and protection,and its main task is to ensure that network resources are 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 are 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.

关 键 词：访问控制 风险评估 神经网络 模糊神经网络 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]