Intrusion Detection Model Using SSMOTE in Power Grid
Xin Zhou, Ning Yu, Jia Zhao, Yao Wu · 2022 IEEE 10th International Conference on Information, Communication and Networks (ICICN) · 2022
The problem of web attack on the power grid has been widely concerned by academia. Aiming at the problem that edge servers in power system are vulnerable to network traffic attack, a SSMOTE oversampling algorithm is proposed. We construct an intrusion detection model for power grid using machine learning algorithms. Based on the benchmark data set of network attacks, the proposed model uses random forest, oversampling and XGBoost algorithms to detect intrusion attacks in the power grid. The comparative experimental results show that the proposed model can effectively detect network traffic attacks.