Abstract
As power systems become increasingly digitalized and intelligent, security threats to power communication networks exhibit characteristics of multi-source, complexity, and stealth. Traditional rule-based or threshold-based security monitoring methods struggle to meet the demands of refined situational awareness. Addressing challenges such as the difficulty of integrating multi-source heterogeneous data, high false alarm rates in alerts, and the complex propagation mechanisms of link congestion, this paper proposes a power network security situational awareness framework that integrates information entropy quantification, LDA semantic topic enhancement, and XGBoost ensemble learning. This approach first performs multi-source data preprocessing through weighted fusion and Kalman smoothing. It then constructs vulnerability severity and attack impact models based on information entropy, enabling a quantifiable representation of the power network security posture. Building upon this foundation, an LDA–XGBoost-based false alarm detection model is developed, significantly enhancing alert credibility and classification accuracy. Additionally, an active–passive adjustment mechanism optimizes communication link congestion states. Experimental results demonstrate that the proposed solution reduces data redundancy by 81.8%, elevates anomaly detection accuracy to 96.8%, achieves a 98.50% resistance rate against encryption cracking, and effectively improves link status indices across multiple cases.
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