Research on abnormal behavior identification method of power network facing unknown attacks

With the widespread application of smart grids, power grids are facing increasingly severe security threats, especially unknown attacks. These attacks often cannot be identified through traditional protective measures, posing serious risks to the safe operation of power systems. This paper proposes a method for identifying abnormal behaviors in power grids when facing unknown attacks. This method utilizes machine learning and data mining technologies to monitor the operational data of power grids in real time. By learning normal behavior patterns, this method can effectively identify abnormal behaviors that deviate from the normal state. Specifically, we use unsupervised learning algorithms to construct an anomaly detection model and design a real-time monitoring system to quickly respond to potential threats. The system can quickly complete anomaly detection and response, demonstrating good real-time performance and adaptability. This study provides an innovative solution for power grid security protection, significantly improving the defense capability against unknown attacks. Future research will continue to explore the application of advanced technologies such as deep learning in the screening of abnormal behaviors, in order to further enhance the security of power grids.