摘要
5G在工业互联网领域的广泛应用给网络安全带来新的挑战。针对现有安全风险评估方法难以适应5G架构特点的问题,提出基于机器学习的风险度量方法,构建包含威胁概率和影响程度的评估指标体系,设计融合主动探测与被动监测的数据获取方案,开发基于随机森林算法的度量模型。在石化工业园区的实践验证表明,该方法能有效识别网络安全风险,为工业互联网安全防护提供重要支撑。
The wide application of 5G in the field of industrial Internet brings new challenges to network security.In view of the problem that the existing security risk assessment method is difficult to adapt to the characteristics of the 5G architecture,this paper proposes a risk measurement method based on machine learning,builds an evaluation index system that includes threat probability and impact degree,designs a data acquisition scheme that integrates active detection and passive monitoring,and develops a measurement model based on stochastic forest algorithms.Practice verification in the petrochemical industrial park shows that this method can effectively identify network security risks and provide important support for industrial Internet security protection.
作者
刘堂伟
LIU Tangwei(Shanghai Branch of CCOC Information Technology Co.,Ltd.,Shanghai 200335,China)
出处
《智能物联技术》
2025年第5期96-100,共5页
Technology of Io T& AI
关键词
5G
工业互联网
安全风险度量
机器学习
5G
industrial internet
security risk measurement
machine learning
