摘要
目前提出的网络空间拟态安全分层检测技术数据覆盖率较低,因此误差消除率较差。选取一组原始数据特征集,通过特征集构建出一套能够自主分层的数据特征子集,应用此技术来初步降低数据维度,在网络空间中,依据神经网络系统,设立多层次的数据反馈模型,利用反馈模型实现安全检测。实验结果表明,该技术能够有效提高数据覆盖率,增加误差消除率。
At present,the proposed cyberspace pseudo security layered detection technology has low data coverage,so the error elimination rate is poor.A set of original data feature sets is selected,a set of data feature subsets is constructed which can be layered independently through the feature sets,and this technology is applied to reduce the data dimension initially.In the network space,according to the neural network system,the multi⁃level data feedback model is established,and the security detection is realized by using the feedback model.The experimental results show that it can effectively improve the data coverage and increase the error elimination rate.
作者
赵继业
ZHAO Jiye(Department of Information,Guangdong Provincial Hospital of Chinese Medicine,Guangzhou 510120,China)
出处
《电子设计工程》
2021年第19期121-125,共5页
Electronic Design Engineering
基金
国家自然科学基金项目(61971029)
广东省科技项目(018S001411)。
关键词
机器学习
网络空间
拟态安全
分层检测
machine learning
cyberspace
mimic security
hierarchical detection
