摘要
为了提高网络的数据安全传输性能,需要进行网络数据安全风险预测,提出一种基于改进机器学习算法的网络数据安全风险预测算法.构建网络的数据传输信道分布模型,提取网络传输码元比特流的安全风险数据统计特征量,根据风险统计分布特征提取结果进行融合聚类处理,构建网络数据安全风险预测的量化回归分析模型,结合关联特征挖掘方法进行网络数据的安全风险指向性预测,采用机器学习算法进行网络安全风险预测中的收敛性控制,实现网络数据安全风险预测.仿真结果表明,采用该方法进行网络数据安全风险预测的准确性较高,对入侵信息的检测能力较强,提高了网络安全性.
A network data security risk prediction algorithm based on improved machine learning algorithm is proposed.The data transmission channel distribution model of network was constructed,and the statistical characteristics of the security risk data of the transmission symbol bit stream of network were extracted.The fusion clustering processing was carried out according to the risk statistical distribution feature extraction.The quantitative regression analysis model of network data security risk prediction was constructed,and the security risk directivity prediction of network data was carried out by combining the association feature mining method.The convergence control of network security risk prediction was carried out by using a machine learning algorithm,and the network data security risk prediction was realized.The simulation results show that the proposed method is accurate in predicting the network data security risks and has a strong ability to detect intrusion information,thus improving network security.
作者
韩高峰
钟元权
HAN Gao-feng;ZHONG Yuan-quan(School of Computer Engineering,Anhui Wenda University of Information Engineering,Hefei 230000,China)
出处
《内蒙古民族大学学报（自然科学版）》
2020年第1期30-35,共6页
Journal of Inner Mongolia Minzu University：Natural Sciences
基金
安徽省级重点教研项目(2018jyxm1394)
关键词
进机器学习
网络数据
安全风险预测
挖掘
Advanced machine learning
Network data
Security risk prediction
Mining
作者简介
韩高峰,安徽文达信息工程学院计算机工程学院讲师,硕士.
