A Temporal Knowledge Graph Application for Network Security of Power Monitoring System Based on KNN and SVM
- Resource Type
- Conference
- Authors
- Sun, Yangsheng; Duo, Zhilin; Jie, Ziguang; Wang, Hao
- Source
- 2022 IEEE 10th International Conference on Information, Communication and Networks (ICICN) Information, Communication and Networks (ICICN), 2022 IEEE 10th International Conference on. :292-296 Aug, 2022
- Subject
- Communication, Networking and Broadcast Technologies
Knowledge engineering
Support vector machines
Supervised learning
Data visualization
Network security
Predictive models
Data models
temporal knowledge graph
KNN
SVM
network security
power monitoring system
- Language
Static knowledge graphs applied to power monitoring systems can no longer guarantee network security in the current situation, so it is necessary to develop dynamic, time-scale knowledge graphs in the current context. This paper proposes a temporal knowledge graph application based on KNN and SVM. Curves with multiple K-values are used in the KNN model to determine feature importance and filter data noise, and a supervised learning classification SVM approach is used to predict four-dimensional data. The ultimate goal is to generate multiple temporal knowledge graphs and use them in a power monitoring system to ensure network security.