Research and Application of Cybersecurity Situation Awareness for Smart Grid Power Control System

Cybersecurity situational awareness technology holds paramount significance within the network environment of power monitoring systems. It serves to promptly detect and address various network threats, thereby safeguarding sensitive information and data against malicious activities. Leveraging innovative technologies such as machine learning, artificial intelligence, and big data, the cybersecurity situational awareness of power monitoring systems can unravel inherent logical relationships among datasets. Presently, the cybersecurity of power monitoring systems confronts an array of risks, exposing certain inadequacies in corresponding defense measures. In response to these challenges, this paper introduces cutting-edge technologies pertaining to the cybersecurity situational awareness platform for power monitoring systems. These encompass a multidimensional security event correlation analysis model, an anomaly detection methodology founded on "baseline learning" for abnormal traffic and behaviors, a model for recognizing attack chains based on attack scenarios, and a power remote control security technology predicated on "address self-verification". We employ data situational awareness, data classification and grading, and data leakage traceability technologies to safeguard the data security of the power control system. Finally, we encapsulate the situational awareness solution for power monitoring systems and its applications, culminating in a synthesis of prospective research for the future.