基于模糊校正的深度时序信息安全评估算法

An evaluation algorithm of information security based on fuzzy adjustment feature matrices and deep time sequence model

针对现有信息安全评估算法对多专家主观态度偏差的处理不足,并且传统时序机器学习模型无法应对时间段内偏差积累等问题,提出一种基于深度模糊校正的深度时序信息安全评估算法。该算法首先通过三角模糊函数构建专家模糊评估指标,并采用改进的加权DS证据推理校正指标,然后创建损失和可能性矩阵特征,最后使用深度时序网络评估信息安全。在MIT数据集上进行了仿真实验,实验分别分析了特征是否能够应对多专家冲突,以及评估算法的正确率,鲁棒性和时间效率等指标。实验结果表明,本文提出的算法拥有更强的模糊评价能力,对专家间的冲突意见处理能力更强,在时序上的信息安全评估更准确,鲁棒性更高,但是算法效率却得到了保持。

The conventional algorithm can not deal with the deviant attitude of different experts in the evaluation of information security.At the same time,it adopts shallow machine learning models and mishandled the deviation accumulating problem.In the study,we proposed a novel evaluation algorithm of information security based on the fuzzy adjustment feature matrices and the deep time sequence model.In the proposed algorithm,the triangle fuzzy function was applied to build the expert‘s evaluation indexes.Then,the improved weight DS theory of evidence was used to adjust the indexes.After that,the loss matrix and possibility matrix are constructed.The information security was evaluated by a deep neural model in proposed model.In the simulations of MIT dataset,we explored whether the feature matrices will cope with the conflicts of experts or not,and the accuracy,performance and robustness of estimation were also checked.It can be found that the proposed algorithm has a better ability to evaluate the fuzzy system,and the ability for coping with the conflicts of experts is also better,achieving a better accuracy and robustness with the reservation of performance.