摘要
针对传统的模糊评价系统存在评价冲突和主观偏差,造成网络安全态势预测出现精度和鲁棒性较低等问题,提出一种结合Dempster-Shafer(D-S)证据理论与循环神经网络的网络安全态势预测算法;首先以专家评价为基础构建网络安全的系统角色,由三角模糊函数获取专家评估指标;然后引入D-S证据理论进行评估指标的筛选、推理和校正,构建网络安全态势损失矩阵和可能性矩阵;最后,以损失矩阵和可能性矩阵为特征输入至循环神经网络中,获取网络安全态势预测结果。仿真实验结果表明,D-S证据理论有效地解决了评价冲突和主观偏差问题,循环神经网络使得网络安全态势预测结果的精度和鲁棒性都得到了提升。
Due to characteristics of evaluation conflicts and subjective biases in conventional fuzzy evaluation system, and problems of low accuracy and robustness performance for the network security situation prediction caused by such characteristics, a novel network security evaluation algorithm based on Dempster-Shafer(D-S) evidence theory and recurrent neural network was proposed. The roles of network security based on the evaluation data of different experts were constructed, and then the triangle fuzzy function was applied to obtain the experts’ evaluation index. The weight D-S evidence theory was used to screen infer, and correct the evaluation index, and the loss and possibility matrixes were constructed. Lastly, the loss and possibility matrixes were imported in RNN model to finish network security evaluation. The results of simulation experiments show that the D-S evidence theory is proved to be able to deal with experts’ collision problems, and the RNN model can improve the precision and robustness of network security situation prediction results.
作者
魏青梅
李宇博
应雨龙
WEI Qingmei;LI Yubo;YING Yulong(Department of Basic Science,Air Force Engineering University,Xi’an 710051,Shaanxi,China;Basic Department,Shaanxi Vocational and Technical College,Xi’an 710038,Shaanxi,China;School of Energy and Mechanical Engineering,Shanghai University of Electric Power,Shanghai 200090,China)
出处
《济南大学学报（自然科学版）》
CAS
北大核心
2020年第3期238-246,共9页
Journal of University of Jinan(Science and Technology)
基金
国家自然科学基金项目(51806135)。
