基于迁移学习的智慧城市物联网安全特征推断研究
Security Feature Inference of Smart City Internet of Things Based on Transfer Learning
李俊蓉 1陈高翔 1应贤儿 1刘建华 1朱子豪1
作者信息
- 1. 绍兴文理学院机械与电气工程学院,浙江绍兴 312000
- 折叠
摘要
在智慧城市物联网中,由于成本的限制,安全态势感知软雷达节点安装稀少,其安全态势感知覆盖范围和粒度都受到了限制,安全感知精度较低.为推断安全态势感知设备无法覆盖的空置区域潜在的安全特征及提高安全识别进精度,设计了一种基于移动设备软雷达的智慧城市物联网安全态势感知评估框架,提出了基于迁移学习的安全潜在特征推断算法,保障智慧城市物联网的安全运行.
Abstract
In the smart city Internet of Things,due to the limitation of cost,the security situation awareness soft radar nodes are installed sparsely,and its security situation awareness coverage and granularity are also limited,and the security perception accuracy is low.In order to infer the potential security features of the vacant area that cannot be covered by the security situation awareness equipment and improve the accuracy of security identification,the author designed a smart city Internet of Things security situation awareness evaluation framework based on mobile device soft radar,proposed a security potential feature inference algorithm based on transfer learning,and guaranteed the safe operation of smart city Internet of Things.
关键词
物联网/网络安全/安全态势感知/迁移学习Key words
Internet of Things/network security/security situation awareness/transfer learning引用本文复制引用
基金项目
绍兴文理学院校级学生科研课题(2022)()
大学生创新创业训练计划(2023)(202310349030)
出版年
2024
NETL
NSTL
万方数据