Lightweight Edge-Based Intrusion Detection for Power Monitoring Systems

Abstract

The network security threats faced by the power monitoring system in the open network environment are becoming increasingly complex, and the attack forms are diversified and concealed. Undetected attacks may lead to abnormal monitoring data, control command failures, or even critical equipment shutdown, thereby threatening the security and stability of the power system. Therefore, network security protection methods that balance detection accuracy, response speed, and edge deployment efficiency have significant engineering application value. To improve the security protection capabilities and real-time response performance in a dynamic network environment, a network security protection method that integrates lightweight intelligent sensing and edge collaborative deployment is constructed. Through multi-source feature fusion and lightweight detection models, combined with the cloud-edge collaborative mechanism, efficient detection and low-latency response to complex attacks are achieved. Experiments showed that the Macro-averaged F1-score reached 0.93, which was 2.08–8.60% higher than that of baseline methods, respectively. The proportion of high-confidence samples of the proposed method was more than 77%, and it could still maintain an attack detection rate of more than 0.94 under different attack traffic proportions. The results show that the method achieves a good balance between detection accuracy, robustness and deployment efficiency, making it suitable for resource-constrained edge devices that require real-time detection and rapid response. This provides technical support for building a low-latency, deployable network security protection system for power monitoring systems.