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A precise sensing method of campus network security situation based on fuzzy clustering algorithm

Ranran Yin & Zhenyu Yang

International Journal of Web Based Communities2025https://doi.org/10.1504/ijwbc.2025.147391article
ABDC C
Weight
0.50

What the paper says

To ensure the safe operation of the campus network and improve the sensing accuracy and convergence speed, a precise sensing method for campus network security situations based on a fuzzy clustering algorithm is proposed. Firstly, the constructed element model is used to extract the situation elements, and the situation information is processed through the non-negative matrix decomposition algorithm. Secondly, the Kalman entropy method is used to estimate the security situation of the whole network of the network campus, and the new information on the network security situation is calculated. Finally, according to the characteristics of campus network security situation awareness, the network security situation awareness is realised through a fuzzy clustering algorithm. The experimental results show that the MAPE value and RMSE value of the proposed method are low, and the RMSE value is maintained below 0.15, the convergence speed is fast, and can comprehensively reflect the network security situation.

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https://doi.org/https://doi.org/10.1504/ijwbc.2025.147391

Or copy a formatted citation

@article{ranran2025,
  title        = {{A precise sensing method of campus network security situation based on fuzzy clustering algorithm}},
  author       = {Ranran Yin & Zhenyu Yang},
  journal      = {International Journal of Web Based Communities},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijwbc.2025.147391},
}

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A precise sensing method of campus network security situation based on fuzzy clustering algorithm

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.