Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Extraction and Evaluation of Cybersecurity Situation Elements in Power Monitoring Networks Based on LDA-XGBoost. |
| Συγγραφείς: |
Zhang, Ruizhi, Zhang, Xiaolin, Jin, Zhiming, Han, Songyi, Dong, Peng |
| Πηγή: |
Journal of Cyber Security & Mobility; 2026, Vol. 15 Issue 1, p215-246, 32p |
| Θεματικοί όροι: |
Internet security, Electric power distribution grids, False alarms, LDAP (Computer network protocol), Computer network protocols |
| Περίληψη: |
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. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
Complementary Index |