Academic Journal
Extraction and Evaluation of Cybersecurity Situation Elements in Power Monitoring Networks Based on LDA-XGBoost.
| 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] |
| Copyright of Journal of Cyber Security & Mobility is the property of River Publishers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: edb DbLabel: Complementary Index An: 192371042 RelevancyScore: 1041 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1041.06604003906 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Extraction and Evaluation of Cybersecurity Situation Elements in Power Monitoring Networks Based on LDA-XGBoost. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Ruizhi%22">Zhang, Ruizhi</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xiaolin%22">Zhang, Xiaolin</searchLink><br /><searchLink fieldCode="AR" term="%22Jin%2C+Zhiming%22">Jin, Zhiming</searchLink><br /><searchLink fieldCode="AR" term="%22Han%2C+Songyi%22">Han, Songyi</searchLink><br /><searchLink fieldCode="AR" term="%22Dong%2C+Peng%22">Dong, Peng</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Cyber Security & Mobility; 2026, Vol. 15 Issue 1, p215-246, 32p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Internet+security%22">Internet security</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+power+distribution+grids%22">Electric power distribution grids</searchLink><br /><searchLink fieldCode="DE" term="%22False+alarms%22">False alarms</searchLink><br /><searchLink fieldCode="DE" term="%22LDAP+%28Computer+network+protocol%29%22">LDAP (Computer network protocol)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+network+protocols%22">Computer network protocols</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Cyber Security & Mobility is the property of River Publishers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=192371042 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.13052/jcsm2245-1439.1518 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 32 StartPage: 215 Subjects: – SubjectFull: Internet security Type: general – SubjectFull: Electric power distribution grids Type: general – SubjectFull: False alarms Type: general – SubjectFull: LDAP (Computer network protocol) Type: general – SubjectFull: Computer network protocols Type: general Titles: – TitleFull: Extraction and Evaluation of Cybersecurity Situation Elements in Power Monitoring Networks Based on LDA-XGBoost. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Ruizhi – PersonEntity: Name: NameFull: Zhang, Xiaolin – PersonEntity: Name: NameFull: Jin, Zhiming – PersonEntity: Name: NameFull: Han, Songyi – PersonEntity: Name: NameFull: Dong, Peng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 22451439 Numbering: – Type: volume Value: 15 – Type: issue Value: 1 Titles: – TitleFull: Journal of Cyber Security & Mobility Type: main |
| ResultId | 1 |