摘要
为保证电力系统中涉密终端的数据传输安全,设计基于机器学习的涉密终端违规外联自动检测系统。采用机器学习技术构建检测系统框架,按照多个阶层实现违规外联的涉密信息过滤;划分违规外联主要方式,通过模块化松耦合模式统一违规外联监测接口;以聚类算法获取涉密信息聚类中心,按照非线性映射关系划分终端违规外联数据类型;基于机器学习计算信息增益率,以特征信息熵为基础自动检测涉密终端违规外联。以3组涉密终端作为测试对象,在多种违规外联方式下验证检测系统的应用效果。结果表明:该系统可实现快速阻断,且具有较高的查全率和查准率,保证涉密信息的安全性。
In order to ensure the security of data transmission of classified terminals in power system,an automatic detection system for illegal external connection of classified terminals based on machine learning is designed.Constructing a detection system framework by adopting a machine learning technology,and filtering secret-related information of violation outreach according to a plurality of levels;dividing a main violation outreach mode,and unifying a violation outreach monitoring interface through a modularized loose coupling mode;acquiring a secret-related information clustering center by a clustering algorithm,and dividing a terminal violation outreach data type according to a nonlinear mapping relationship;The information gain rate is calculated based on machine learning,and the illegal outreach of the secret-related terminal is automatically detected based on the feature information entropy.Taking three groups of classified terminals as the test objects,the application effect of the detection system is verified in a variety of illegal outreach modes.The results show that the system can achieve rapid blocking,and has a high recall rate and precision rate to ensure the security of classified information.
作者
钱锦
徐李冰
李昂
徐汉麟
Qian Jin;Xu Libing;Li Ang;Xu Hanlin(State Grid Hangzhou Power Supply Comapny,Hangzhou 310000,China)
出处
《兵工自动化》
北大核心
2025年第8期73-77,共5页
Ordnance Industry Automation
关键词
机器学习
涉密终端
违规外联
自动检测系统
machine learning
classified terminal
illegal external connection
automatic detection system
