Multidimensional Data Analysis and Algorithm Optimization of Power System Network Security Protection

The enhancement of automation and intelligence levels in power systems, particularly hydroelectric power plants, has led to an increase in cybersecurity risks. The article first outlines the background and challenges of cybersecurity in power systems, including the complexity of network structures, primary threats faced, and limitations of existing protective measures. It then proposes a multidimensional data model based on time, space, threat type, and behavioral characteristics, integrating machine learning, data mining techniques, and algorithm optimization to construct a multilayered, comprehensive cybersecurity analysis model. This model employs Gradient Boosting Decision Trees (GBDT), Isolation Forest (IF), and Long Short-Term Memory networks (LSTM) as its primary algorithmic frameworks and adopts a Stacking strategy to enhance overall predictive performance. Through a case study at a large hydroelectric power plant, the system's effectiveness in improving detection accuracy, reducing false positive and false negative rates, shortening average response times, and decreasing the number of cybersecurity incidents is validated. Finally, the article discusses the challenges and future trends in cybersecurity protection for hydroelectric power plants, proposing recommendations such as strengthening technology research and development, talent cultivation, and international cooperation to address the increasingly complex cybersecurity environment.