云计算环境用户行为信任评估模型构建研究

Construction method of user behavior trust evaluation model in cloud computing environment

针对传统云计算环境信任模型预测精度过低的问题,提出一种基于深度学习的云计算环境用户行为评估模型构建方法。采用预测-残差框架的长短期记忆网络对用户在云计算环境中交互行为集进行时序特征提取,在此基础上结合误差机制修正特征向量,构建云计算环境用户行为信任评估模型。该模型能够实现云计算环境用户访问行为安全的实时监控,解决云计算环境用户实时信任度评估精度过低的问题。根据试验数据表明,云计算环境用户行为信任评估模型构建方法能够有效提升用户可信度实时评估的准确率,可为安全态势分析和推演提供有效的数据支撑。

Aiming at the problem that the prediction accuracy of traditional trust model in cloud computing environment is too low,this paper proposes a method to build user behavior evaluation model in cloud computing environment based on deep learning.The long-term and short-term memory network of prediction residual framework is used to extract the temporal features of users’access behavior in the cloud computing environment.On this basis,combined with the error mechanism to modify the feature vector,the trust evaluation model of users’behavior in the cloud computing environment is constructed.The model can realize real-time monitoring of user behavior security in cloud computing environment,and solve the problem of low trust evaluation of users in cloud computing environment.Experimental results show that the proposed method can effectively improve the accuracy of real-time evaluation of user credibility,and provide data support for later security situation deduction.